research topics on digital image processing

Top 10 Digital Image Processing Project Topics

We guide research scholars in choosing novel digital image processing project topics. What is meant by digital image processing? Digital Image Processing is a method of handling images to get different insights into the digital image. It has a set of technologies to analyze the image in multiple aspects for better human / machine image interpretation . To be clearer, it is used to improve the actual quality of the image or to abstract the essential features from the entire picture is achieved through digital image processing projects.

This page is about the new upcoming Digital Image Processing Project Topics for scholars who wish to create a masterpiece in their research career!!!

Generally, the digital image is represented in the form of pixels which are arranged in array format. The dimension of the rectangular array gives the size of the image (MxN), where M denotes the column and N denotes the row. Further, x and y coordinates are used to signify the single-pixel position of an image. At the same time, the x value increases from left to right, and the y value increases from top to bottom in the coordinate representation of the image. When you get into the DIP research field, you need to know the following key terminologies.

Important Digital Image Processing Terminologies  

• Stereo Vision and Super Resolution
• Multi-Spectral Remote Sensing and Imaging
• Digital Photography and Imaging
• Acoustic Imaging and Holographic Imaging
• Computer Vision and Graphics
• Image Manipulation and Retrieval
• Quality Enrichment in Volumetric Imaging
• Color Imaging and Bio-Medical Imaging
• Pattern Recognition and Analysis
• Imaging Software Tools, Technologies and Languages
• Image Acquisition and Compression Techniques
• Mathematical Morphological Image Segmentation

Image Processing Algorithms

In general, image processing techniques/methods are used to perform certain actions over the input images, and according to that, the desired information is extracted in it. For that, input is an image, and the result is an improved/expected image associated with their task. It is essential to find that the algorithms for image processing play a crucial role in current real-time applications. Various algorithms are used for various purposes as follows, 

• Digital Image Detection
• Image Reconstruction
• Image Restoration
• Image Enhancement
• Image Quality Estimation
• Spectral Image Estimation
• Image Data Compression

For the above image processing tasks, algorithms are customized for the number of training and testing samples and also can be used for real-time/online processing. Till now, filtering techniques are used for image processing and enhancement, and their main functions are as follows, 

• Brightness Correction
• Contrast Enhancement
• Resolution and Noise Level of Image
• Contouring and Image Sharpening
• Blurring, Edge Detection and Embossing

Some of the commonly used techniques for image processing can be classified into the following, 

• Medium Level Image Processing Techniques – Binarization and Compression
• Higher Level Image Processing Techniques – Image Segmentation
• Low-Level Image Processing Techniques – Noise Elimination and Color Contrast Enhancement
• Recognition and Detection Image Processing Algorithms – Semantic Analysis

Next, let’s see about some of the traditional image processing algorithms for your information. Our research team will guide in handpicking apt solutions for research problems . If there is a need, we are also ready to design own hybrid algorithms and techniques for sorting out complicated model . 

Types of Digital Image Processing Algorithms

• Hough Transform Algorithm
• Canny Edge Detector Algorithm
• Scale-Invariant Feature Transform (SIFT) Algorithm
• Generalized Hough Transform Algorithm
• Speeded Up Robust Features (SURF) Algorithm
• Marr–Hildreth Algorithm
• Connected-component labeling algorithm: Identify and classify the disconnected areas
• Histogram equalization algorithm: Enhance the contrast of image by utilizing the histogram
• Adaptive histogram equalization algorithm: Perform slight alteration in contrast for the  equalization of the histogram
• Error Diffusion Algorithm
• Ordered Dithering Algorithm
• Floyd–Steinberg Dithering Algorithm
• Riemersma Dithering Algorithm
• Richardson–Lucy deconvolution algorithm : It is also known as a deblurring algorithm, which removes the misrepresentation of the image to recover the original image
• Seam carving algorithm : Differentiate the edge based on the image background information and also known as content-aware image resizing algorithm
• Region Growing Algorithm
• GrowCut Algorithm
• Watershed Transformation Algorithm
• Random Walker Algorithm
• Elser difference-map algorithm: It is a search based algorithm primarily used for X-Ray diffraction microscopy to solve the general constraint satisfaction problems
• Blind deconvolution algorithm : It is similar to Richardson–Lucy deconvolution to reconstruct the sharp point of blur image. In other words, it’s the process of deblurring the image.

Nowadays, various industries are also utilizing digital image processing by developing customizing procedures to satisfy their requirements. It may be achieved either from scratch or hybrid algorithmic functions . As a result, it is clear that image processing is revolutionary developed in many information technology sectors and applications.  

Digital Image Processing Techniques

• In order to smooth the image, substitutes neighbor median / common value in the place of the actual pixel value. Whereas it is performed in the case of weak edge sharpness and blur image effect.
• Eliminate the distortion in an image by scaling, wrapping, translation, and rotation process
• Differentiate the in-depth image content to figure out the original hidden data or to convert the color image into a gray-scale image
• Breaking up of image into multiple forms based on certain constraints. For instance: foreground, background
• Enhance the image display through pixel-based threshold operation 
• Reduce the noise in an image by the average of diverse quality multiple images 
• Sharpening the image by improving the pixel value in the edge
• Extract the specific feature for removal of noise in an image
• Perform arithmetic operations (add, sub, divide and multiply) to identify the variation in between the images 

Beyond this, this field will give you numerous Digital Image Processing Project Topics for current and upcoming scholars . Below, we have mentioned some research ideas that help you to classify analysis, represent and display the images or particular characteristics of an image.

Latest 11 Interesting Digital Image Processing Project Topics

• Acoustic and Color Image Processing
• Digital Video and Signal Processing
• Multi-spectral and Laser Polarimetric Imaging
• Image Processing and Sensing Techniques
• Super-resolution Imaging and Applications
• Passive and Active Remote Sensing
• Time-Frequency Signal Processing and Analysis
• 3-D Surface Reconstruction using Remote Sensed Image
• Digital Image based Steganalysis and Steganography
• Radar Image Processing for Remote Sensing Applications
• Adaptive Clustering Algorithms for Image processing

Moreover, if you want to know more about Digital Image Processing Project Topics for your research, then communicate with our team. We will give detailed information on current trends, future developments, and real-time challenges in the research grounds of Digital Image Processing.

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Digital Image Processing: Advanced Technologies and Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section " Computing and Artificial Intelligence ".

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Dear Colleagues,

Throughout the 21 st century, the human demand for information has been increasing every day. The choice of an electronic imaging device is related to its application. With the rapid technological developments and use of mobile devices and social media, humans are consistently exposed to a significant amount of information, including digital images and videos. With every minute that passes, the internet is flooded with huge amounts of digital content. Hence, digital imaging has obtained a substantial role in various scientific expeditions, for instance, in image enhancement, restorations, and various object recognition tasks. Often, the colors and contrast of many real life images degrade abruptly due to various factors, such as insufficient lighting, excessive light absorption, scattering, and of course limitations of the imaging devices themselves. Similarly, the hardware restrictions of image or video capturing devices also affects the imaging quality. Moreover, the selective absorption and scattering of light tends to cause color deviations in many real life images, which results in a blurry image and poor contrast. Furthermore, in various situations, digital images are distorted, which sooner or later degrades the visual experience for human viewers. For instance, adverse weather conditions, such as rain, snow, fog, or cloudy environments result in blurry images along with color distortions. Although imaging equipment with better embedded hardware can improve the image quality to a certain extent, in many situations, its adaptability is poor. Hence, the quality of the acquired images is non-satisfactory.

This Special Issue will focus on digital imaging strategies, with the aim of processing, analyzing, and investigating imaging and all the latest methods involved in handling them. Manuscripts are invited in all these different multidisciplinary areas, but are not limited to them. Recently, interest in computer vision, machine learning, and deep learning has grown significantly. Therefore, manuscripts that explore the utility of such tools in exploring advanced technologies and applications are also encouraged.

In this Special Issue, we invite authors to submit original research papers, reviews, and viewpoint articles that are related to recent advances at all levels of the applications and technologies of imaging and signal analysis. We are open to papers that address a diverse range of topics, from foundational issues up to novel algorithms that aim to provide state-of-the-art solutions and technological systems for practical and feasible applications.

The Special Issue on “Digital Image Processing: Advanced Technologies and Applications” covers rising trends in image theory additions, original algorithms, and novel architectures to capture, form, and display digital images, subsequent processings, communications, analysis, videos, and multidimensional systems and signals in an extensive diversity of applications. Topics of interest include, but are not limited to:

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Digital Image Processing

Concepts, Algorithms, and Scientific Applications

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Scripps Institution of Oceanography Physical Oceanography Research Division A-030, University of California San Diego, La Jolla, USA

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Mathematical Methods in Image Processing and Computer Vision

Image Processing and ‘Noise Removal Algorithms’—The Pdes and Their Invariance Properties & Conservation Laws

• Mustererkennung
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Table of contents (17 chapters)

Front matter, introduction.

Bernd Jähne

Image Formation and Digitization

Space and wave number domain, neighborhoods, mean and edges, local orientation, segmentation, classification, reconstruction from projections, displacement vectors, displacement vector fields, space-time images, back matter, authors and affiliations, bibliographic information.

Book Title : Digital Image Processing

Book Subtitle : Concepts, Algorithms, and Scientific Applications

Authors : Bernd Jähne

DOI : https://doi.org/10.1007/978-3-662-11565-7

Publisher : Springer Berlin, Heidelberg

eBook Packages : Springer Book Archive

Copyright Information : Springer-Verlag Berlin Heidelberg 1991

eBook ISBN : 978-3-662-11565-7 Published: 09 March 2013

Edition Number : 1

Number of Pages : XIII, 383

Number of Illustrations : 297 b/w illustrations, 88 illustrations in colour

Topics : Communications Engineering, Networks , Computer Appl. in Life Sciences , Earth Sciences, general , Biological and Medical Physics, Biophysics , Artificial Intelligence , Image Processing and Computer Vision

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Editorial article, editorial: current trends in image processing and pattern recognition.

• PAMI Research Lab, Computer Science, University of South Dakota, Vermillion, SD, United States

Editorial on the Research Topic Current Trends in Image Processing and Pattern Recognition

Technological advancements in computing multiple opportunities in a wide variety of fields that range from document analysis ( Santosh, 2018 ), biomedical and healthcare informatics ( Santosh et al., 2019 ; Santosh et al., 2021 ; Santosh and Gaur, 2021 ; Santosh and Joshi, 2021 ), and biometrics to intelligent language processing. These applications primarily leverage AI tools and/or techniques, where topics such as image processing, signal and pattern recognition, machine learning and computer vision are considered.

With this theme, we opened a call for papers on Current Trends in Image Processing & Pattern Recognition that exactly followed third International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R), 2020 (URL: http://rtip2r-conference.org ). Our call was not limited to RTIP2R 2020, it was open to all. Altogether, 12 papers were submitted and seven of them were accepted for publication.

In Deshpande et al. , authors addressed the use of global fingerprint features (e.g., ridge flow, frequency, and other interest/key points) for matching. With Convolution Neural Network (CNN) matching model, which they called “Combination of Nearest-Neighbor Arrangement Indexing (CNNAI),” on datasets: FVC2004 and NIST SD27, their highest rank-I identification rate of 84.5% was achieved. Authors claimed that their results can be compared with the state-of-the-art algorithms and their approach was robust to rotation and scale. Similarly, in Deshpande et al. , using the exact same datasets, exact same set of authors addressed the importance of minutiae extraction and matching by taking into low quality latent fingerprint images. Their minutiae extraction technique showed remarkable improvement in their results. As claimed by the authors, their results were comparable to state-of-the-art systems.

In Gornale et al. , authors extracted distinguishing features that were geometrically distorted or transformed by taking Hu’s Invariant Moments into account. With this, authors focused on early detection and gradation of Knee Osteoarthritis, and they claimed that their results were validated by ortho surgeons and rheumatologists.

In Tamilmathi and Chithra , authors introduced a new deep learned quantization-based coding for 3D airborne LiDAR point cloud image. In their experimental results, authors showed that their model compressed an image into constant 16-bits of data and decompressed with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction. Authors claimed that their method can be compared with previous algorithms/techniques in case we consider the following factors: space and time.

In Tamilmathi and Chithra , authors carefully inspected possible signs of plant leaf diseases. They employed the concept of feature learning and observed the correlation and/or similarity between symptoms that are related to diseases, so their disease identification is possible.

In Das Chagas Silva Araujo et al. , authors proposed a benchmark environment to compare multiple algorithms when one needs to deal with depth reconstruction from two-event based sensors. In their evaluation, a stereo matching algorithm was implemented, and multiple experiments were done with multiple camera settings as well as parameters. Authors claimed that this work could be considered as a benchmark when we consider robust evaluation of the multitude of new techniques under the scope of event-based stereo vision.

In Steffen et al. ; Gornale et al. , authors employed handwritten signature to better understand the behavioral biometric trait for document authentication/verification, such letters, contracts, and wills. They used handcrafter features such as LBP and HOG to extract features from 4,790 signatures so shallow learning can efficiently be applied. Using k-NN, decision tree and support vector machine classifiers, they reported promising performance.

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The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Santosh, KC, Antani, S., Guru, D. S., and Dey, N. (2019). Medical Imaging Artificial Intelligence, Image Recognition, and Machine Learning Techniques . United States: CRC Press . ISBN: 9780429029417. doi:10.1201/9780429029417

CrossRef Full Text | Google Scholar

Santosh, KC, Das, N., and Ghosh, S. (2021). Deep Learning Models for Medical Imaging, Primers in Biomedical Imaging Devices and Systems . United States: Elsevier . eBook ISBN: 9780128236505.

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Santosh, KC (2018). Document Image Analysis - Current Trends and Challenges in Graphics Recognition . United States: Springer . ISBN 978-981-13-2338-6. doi:10.1007/978-981-13-2339-3

Santosh, KC, and Gaur, L. (2021). Artificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact . Spain: SpringerBriefs in Computational Intelligence Series . ISBN: 978-981-16-6768-8. doi:10.1007/978-981-16-6768-8

Santosh, KC, and Joshi, A. (2021). COVID-19: Prediction, Decision-Making, and its Impacts, Book Series in Lecture Notes on Data Engineering and Communications Technologies . United States: Springer Nature . ISBN: 978-981-15-9682-7. doi:10.1007/978-981-15-9682-7

Keywords: artificial intelligence, computer vision, machine learning, image processing, signal processing, pattern recocgnition

Citation: Santosh KC (2021) Editorial: Current Trends in Image Processing and Pattern Recognition. Front. Robot. AI 8:785075. doi: 10.3389/frobt.2021.785075

Received: 28 September 2021; Accepted: 06 October 2021; Published: 09 December 2021.

Edited and reviewed by:

Copyright © 2021 Santosh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: KC Santosh, [email protected]

This article is part of the Research Topic

Current Trends in Image Processing and Pattern Recognition

Research Topics

Biomedical Imaging

The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.

Computer Vision

Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.

Image Segmentation/Classification

Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.

Multiresolution   Techniques

The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.

Remote Sensing

Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.

Scientific Imaging

Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.

Stochastic Models

In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.

Video Analysis

Video analysis is a field within  computer vision  that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.

Evolutionary Deep Intelligence

Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.

Discovery Radiomics

Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management. 

Sports Analytics

Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.

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• Digital Image Processing Topics

Learn more about Image Processing here, we have listed latest list of digital image processing topics for thesis and research. Digital image processing furnishes the well-established platform for employing complex approaches for processing digital images to enhance image interpretation and representation. And, it can even perform operations that are hard to work with analog processing methods. Hence, it is widely used in many research fields that deal with image processing areas.

What are the Types of Image Processing?

In general, there are two major categories of techniques for image processing. And they are analog image processing and digital image processing . Analog image processing takes place in 2D analog signals, which are used for photographic films and printouts. Here, image analysts use various chemical components to develop and visualize the image. But in the case of digital image processing, it is processed through intelligent algorithms and approaches in the digital computer where the output is also in the digital image.  

We provide the following descriptions for any digital image processing topics, 

• Different Digital Image Processing Technologies
• Fundamental Theories and Perception
• Various Research Areas that are currently evolved
• Related Conceptual Concepts / Schemes
• Problem Solving Approaches and Procedures
• Mathematical and Numerical Functions Equation / Formulae
• Own Algorithm Pseudocode for Complex Problem
• Overall System Architecture, UML Diagrams, and Data Flow Chart
• Comparative Study of Different Scenarios and Datasets
• System Performance Evaluation and Analysis
• Information about the Simulation Blocks and Test Results
• Final Discussion on Experimental Outcome

This page is about the emerging Digital Image Processing Topics and current research areas with future advances!!!

 Image processing is an extensive research area that can be recognized in all the research fields in some aspects. That is to say; it spreads its footprints in all the dimensions of the real-world environment, which ranges from leaf identification to patient disorder prediction and analysis. For your reference,

here we have listed few important image processing applications .

• Vision-assisted Robotic System.
• Bar Code and QR Code Scanner / Reader
• Social Websites and Apps Development (For instance: Instagram and Snapchat)
• iPhone’s Face Recognition and Unlock System
• Digital-based Computational photography
• Automated assembly based on Sensor for Object Detection
• Advance Processing of Astronomical or Planetary images (For instance: Images of Space Probe and Hubble Telescope Pictures)
• Remote Sensing and Visual Interpretation (For instance: Satellite and Aerial Image)
• Automated Optical or Handwritten Character Recognition (For instance: License Plate and Zip Code)
• Industrial Manufacturing Applications (For instance: Product Optical Sorting and Assessment)
• Biometric Authentication Technologies (For instance: Face, Iris and Finger Print Identification)
• Bio-Medical Imaging and Processing (For instance: Blood Cell Microscope Images and Chest Radiograph Interpretation)

Then, our research team has given you the algorithms that are broadly used in digital image processing projects . Each algorithm has different nature to support various needs of the image processing operations. Here, we have listed some algorithms with their usage based on common categories.  

What are the Important Algorithms in Image Processing? 

• Classification Ensembles Purpose : Improve the   predictive performance and image classification
• Algorithms : Random forest algorithms, random subspace learning, and boosting (Catboost, XGboost)
• Purpose:  Search and   categorize the pattern
• Algorithms:  KD tree-based K nearest neighbors classification algorithm
• Purpose : Analyze various elements to forecast the multi-class probability
• Algorithms : Multinomial and Gaussian naive Bayes algorithms
• Purpose:  Classify into multiple different classes
• Algorithms:  Binary decision trees
• Purpose:  Interpretable model is made up of univariate and bivariate shape methods used to stop the overfitting issue
• Algorithms:  Binary classification
• Purpose:  Detect hidden relations between attributed
• Algorithms:  Binary-Neural networks and multiclass-Neural network classifications
• Purpose: Normalizing and minimizing the dimension
• Algorithms: Quadratic / linear discriminant analysis algorithms
• Purpose:  Assess the performance of flowing data
• Algorithms : Fit classification model
• Purpose:  Distinguish the labeled and unlabeled data
• Algorithms:  Self-training learning and Graph-based learning algorithms
• Purpose:  Incorporate decision plane method for analyzing and classify the data
• Algorithms:  Multi-class and binary SVM classification

For your benefit ,  our resource team has shared some advanced research dip project ideas that create an incredibly positive impact on future image and video processing projects . Here, we have given only a few digital image processing topics for your awareness; more than this, we have a massive number of futuristic research topics. You can make a bond with us to know other technical advancements.  

Latest Digital Image Processing Topics

• Applications: Advanced Optimization Methods for Multi-variable problems
• Object Detection in Real-time Video Streaming
• DL assisted Video Analysis
• Learning-based Object Identification
• Object Classification Using Learning Techniques
• Stenography
• Watermarking
• Bag-of-Features Method for Image Classification
• Medical Fusion Methods for Image enhancements
• Custom based Bag of Features / Models for Image Retrieval

For illustration purposes, here we have taken the one real-time sample application as “Root Analysis in Precision Agriculture.” Here, we itemized the implementation plan for the computing root volume for analyzing the productivity of the plant. It helps to increase plant growth and production . In addition, it can also be extended in other soil crops and hydroponics.  

Root Analysis in Precision Agriculture Applications 

• Step 1 –  Convert the RGB image to a Gray-scale image
• Step 2 –  Enhance the Contrast of the image
• Step 3 –  Perform Binarize to avoid background noise
• Step 4 –  Employ the suitable filter and mask techniques
• Minimize the noise in the image
• Delimit area of interest (mask)
• Step 5 –  Implement “AND” operation among mask and processed image

Next, we can see the development of technologies and tools for digital image processing projects . Each tool has special functionalities and different characteristics. So, when you handpick the tool, consider the features and think about which tool gives accurate results for the proposed topic .  

Digital Image Processing Tools 

• And also many more

Below, our research team has given the dataset that is popularly used while practically implementing the Digital Image Processing Topics . Our developers also help you in selecting datasets since datasets are more important for processing and analysis.  

Famous Digital Image Processing Datasets 

Waymo Open Dataset

• Used for training self-driving vehicles.
• Contains driving videos with marked objects and followings,
• 3,000 driving videos
• 600,000 frames
• 22 million 2D object boundaries
• 25 million 3D object boundaries
• Intended to exclude the video uniformity issue
• Include several video processing options: lighting, construction sites, weather, cyclists, and pedestrians
• Different kinds of data will eventually increase the model’s generalization capability

SketchTransfer

• Used for simplification purposes by using neural networks
• Include real-world unlabeled sketches and labeled images

YouTube-8M Segments

• Include dynamically changing content with the followings,
• 1000 classes (nearly)
• 23700 designs (nearly)

DroneVehicle

• Used for totaling objects present in the drone images
• It contains 441642 objects and 31,064 images with object class and boundaries
• Include 15532 RGB drone shots and infrared shots for each and every image

If you are interested to know new updates on current Digital Image Processing Topics , then contact our team. Further, we also help you in developing your own novel research ideas.

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              Digital image processing thesis topics are actively chosen these days, considering the scope of the topic in the near future. Here is a detailed understanding of doing projects in digital image processing . Digital image processing is the process by which digital images are modified according to the user’s wish.  Initially, images are an array of two-dimensional points arranged into columns and rows. First, let us start with its working. It can be made into the following.

• Black and white image
• 8-bit image
• 16-bit color format

HOW DOES DIGITAL IMAGE PROCESSING WORK?

It is one of the most fundamental questions that have to be answered before dwelling deep into the topic. Digital image processing projects are the favourite area of research for our expert team. They are currently guiding several projects on advanced image processing in the digital world. They suggest the following steps as the basic functionality of digital image processing.

• Acquiring inputs in the form of both video and image
• Analysis of the input
• Extraction of useful information from it through manipulation 
• Processing of output
• Reporting of the final output

In this way, the digital image processing method works. Your project can have an objective to improve upon these steps by implementing current developments like AI and Internet of Things projects into it. Do you feel it would be easy for you if someone already experienced in the field of digital

Image processing methods research helps you in your project? If so, then you have found the right place to get assistance from. We provide the best online research guidance for projects related to digital image processing . We have the most favoured online research experts who can help you do the best projects on the topic. Please continue reading to know more about our digital image processing projects.

WHAT ARE THE OPERATIONS IN DIGITAL IMAGE PROCESSING?

As you might know, there are various processes involved in the techniques of digital image processing. We are currently developing projects on all these steps. Our projects mostly spiral around these topics with the aim of improving their efficiency. The following are the  different steps involved in the functioning of digital image processing .

• Image retrieval (extracting useful images from the input)
• Detecting objects (object recognition)
• Extraction of content (essential content is extracted)
• Image preprocessing (denoising, restoring, enhancing contrast, etc.)
• Detection of the object (object recognition)

These steps are very significant for the processing of digital images. Algorithms are developed so as to achieve more efficiency in each of these steps.  These algorithms can be evaluated based on the performance and the quality of output obtained . Our technical team is building various methods to enhance image quality. 

We provide you support for digital image processing thesis topics too. Our developers and writers are well qualified and are highly experienced in producing standard theses and summaries. So you can rely on them for any support regarding your dip thesis . Now let us look into some of the performance metrics used for evaluating the algorithms used in digital image processing .

HOW IS IMAGE QUALITY MEASURED?

The quality image is a direct outcome of the algorithm used for processing digital images. The evaluation of such algorithms is based on the following factors.

• ROC and AUC curve
• Recall 
• Sensitivity
• Precision 
• Specificity
• Accuracy 

All our projects have shown great results with respect to these metrics. You can get in touch with us to know more about the projects that we delivered. We will provide you the details of the performance of our projects when they were implemented in real-time dip projects using python . 

Along with these metrics, some pre-and post-processing metrics should look upon to design your project on digital image processing . Let us see about those metrics in the following.

PERFORMANCE ANALYSIS IN DIGITAL IMAGE PROCESSING

PREPROCESSING METRICS

The following are the preprocessing metrics used in evaluating digital image processing methods.

• Root mean squared error or RMSE
• Structural Similarity Index or SSIM
• Patch-based contrast quality index or PCQI
• Blink or reference less image spatial quality evaluator or BRISQUE
• Mean Squared Error or MSE
• Peak Signal to Noise Ratio or PSNR
• In contrast to noise ratio or CNR
• Colour image Quality Measure or CIQM

Your project should focus on showing good results with respect to these metrics. Our engineers can guide you in such a way to achieve greater results in performance metrics. Get in touch with us and have a talk with our experts on choosing your digital image processing thesis topics . We will now provide you details of post-processing metrics.

POSTPROCESSING METRICS

The following post-processing metrics have to be remembered in the case of digital image processing techniques

• Kappa quadratic weight
• Kappa coefficient 
• Mean absolute error
• Accuracy(total)
• Kappa linear weight
• Root mean square error
• Rate of error
• Confusion matrix

When your research project excels in these measurements, your project will be appreciated. We are ready to stand by your side to make your project a huge success. Now let us see about some important research ideas in digital image processing.

RESEARCH IDEAS IN DIGITAL IMAGE PROCESSING  THESIS TOPICS

The following are the most important areas of research in digital image processing based on the current trends. 

• Detecting number plate (segmentation and classification)
• Detecting lung cancer (CNN approach)
• Autonomous navigation
• Advanced and recent methods for processing images
• Compression of video and image(for reducing size) 
• Scene understanding
• Detecting copy-move forgery (by extracting textual feature)
• Detection of diabetic retinopathy by neural network method
• Multiple object detection
• Face spoof detection (method of extracting eigen feature)

We have reviewed and monitored projects with these metrics. World-class experts with us are highly experienced in writing your thesis so as to show better results in these metrics. As algorithms for this performance efficiency basis , let us see more about the different types of algorithms that are popular in digital image processing.

IMPORTANT ALGORITHMS FOR DIGITAL IMAGE PROCESSING

The following are the standard algorithms for digital image processing

• Conditional GANs
• Deep convolutional GANs 

Currently, very few experts in handling these algorithms around the world  are well experienced in dealing with these algorithms. They are updating them every now and then to make themselves undeniable choices for research support in digital image processing. Now let us see in more detail about digital image processing projects using MATLAB in image analysis.

WHAT IS IMAGE ANALYSIS IN MATLAB? 

MATLAB plays a key role in Analysing images on the following grounds.

• Detection of edges
• Counting (objects)
• Shape finding
• Noise removal
• Calculation of statistics (analysis of texture and quality of the image)

You might have been more familiar with using MATLAB.  Our engineers have been phenomenal in handling MATLAB techniques for many ideal case applications.  So you can know more about the practical difficulties that they faced and the ways in which they overcame these issues and made their projects more ideal than others. 

IMAGE PROCESSING TECHNIQUES FOR IMAGE ANALYSIS

Extraction of useful information from an image is called image analysis. The following are the categories of image analysis.

• Region analysis (extraction of statistical information)
• Segmentation of image (for distinguishing objects and regions)
• Removing noise (with deep learning and morphological filtering)
• Enhancement of image (displaying and analyzing images)

MATLAB functions are quite popular for usage in analyzing medical images . Let us see about the functions of MATLAB used for image analysis in the following section.

MATLAB FUNCTIONS FOR IMAGE ANALYSIS

The following MATLAB functions are used for image analysis.

• bwselect3 (selection of objects)
• imgradientxyz (finding 3D image direction and magnitude of gradient)
• imhist (image histogram data)
• edge3 (3D intensity volume – finding edges)
• imgradientxyz (finding direction gradients of 3D images)
• regionprops3 (measurement of volume of regions in 3D volumetric images)

Now let us see more about MATLAB functions for the segmentation of images.

MATLAB FUNCTIONS FOR IMAGE SEGMENTATION

There are some critical MATLAB functions used for image segmentation. They are listed below.

• Bfscore (outlines image segmentation score)
• Gradientweight (calculation of weights)
• Imsegfmm (segmentation of binary image)
• Jaccard (finding Jaccard similarity coefficient)
• Active contour (segmentation of images on fore and background)
• Dice (for Sorensen-dice similarity coefficient)
• Graydiffweight (image pixel weight calculation)
• imsegkmeans3 (volume segmentation based on k-means clustering)
• superpixels3 (oversegmentation of 3D superpixel)

Our experts can give you complete support and guidance in any digital image processing thesis topic . You can reach out to us regarding any type of research support, and we here provide you details of all basics about digital image processing. Advanced ideas are also readily available with us. We will stay with you in your entire research journey.

• LATEST DIGITAL IMAGE PROCESSING THESIS TOPICS

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20+ Image Processing Projects Ideas in Python with Source Code

Image Processing Projects Ideas in Python with Source Code for Hands-on Practice to develop your computer vision skills as a Machine Learning Engineer.

Perhaps the great French military leader Napolean Bonaparte wasn't too far off when he said, “A picture is worth a thousand words.” Ignoring the poetic value, if just for a moment, the facts have since been established to prove this statement's literal meaning. Humans, the truly visual beings we are, respond to and process visual data better than any other data type. The human brain is said to process images 60,000 times faster than text. Further, 90 percent of information transmitted to the brain is visual. These stats alone are enough to serve the importance images have to humans.

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Therefore, the domain of image processing, which deals with enhancing images and extracting useful information from them, has been growing exponentially since its inception. This process has developed and augmented popular platforms and libraries like MATLAB, scikit-image, and OpenCV . The technology forerunners and the world-renowned conglomerates such as Google, Apple, Microsoft, and Amazon dabble in Image processing. Therefore, the hands-on experience working on image processing projects can be an invaluable skill.

Table of Contents

Image processing projects for beginners, 1) grayscaling images, 2) image smoothing, 3) edge detection, 4) skew correction, 5) image compression using matlab, 6) image effect filters , 7) face detection, 8) image to text conversion using matlab, 9) watermarking, 10) image classification using matlab, 11) background subtraction, 12) instance segmentation, 13) pose recognition using matlab, 14) medical image segmentation, 15) image fusion, 16) sudoku solver, 17) bar-code detection, 18) automatically correcting images’ exposure, 19) quilting images and synthesising texture, 20) signature verifying system, what is image processing with example, what can be done with image processing, which is the best software for image processing, 20+ image processing projects ideas.

With the vast expectations the domain bears on its shoulders, getting started with Image Processing can unsurprisingly be a little intimidating. As if to make matters worse for a beginner, the myriad of high-level functions implemented can make it extremely hard to navigate. Since one of the best ways to get an intuitive understanding of the field can be to deconstruct and implement these commonly used functions yourself, the list of image processing projects ideas presented in this section seeks to do just that! 

New Projects

This section has easy image processing projects ideas for novices in Image processing. You will find this section most helpful if you are a student looking for image processing projects for the final year.

Grayscaling is among the most commonly used preprocessing techniques as it allows for dimensionality reduction and reduces computational complexity. This process is almost indispensable even for more complex algorithms like Optical Character Recognition, around which companies like Microsoft have built and deployed entire products (i.e., Microsoft OCR).

The output image shown above has been grayscaled using the rgb2gray function from scikit-image. (Image used from Image Processing Kaggle)

There are plenty of readily available functions in OpenCV, MATLAB, and other popular image processing tools to implement a grayscaling algorithm. For this image processing project, you could import the color image of your choice using the Pillow library and then transform the array using NumPy . For this project, you are advised to use the Luminosity Method, which uses the formula 0.21*R+0.72*G+0.07*B. The results look similar to the Grayscale image in the figure with minor variations in contrast because of the difference in the formula used.  Alternatively, you could attempt to implement other Grayscaling algorithms like the Lightness and the Average Method.

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Image smoothing ameliorates the effect of high-frequency spatial noise from an image. It is also an important step used even in advanced critical applications like medical image processing, making operations like derivative computation numerically stable.

For this beginner-level image processing project, you can implement Gaussian smoothing. To do so, you will need to create a 2-dimensional Gaussian kernel (possibly from one-dimensional kernels using the outer product) by employing the NumPy library and then convoluting it over the padded image of your choice. The above output has been obtained from the scikit-image with the Multi-dimensional Gaussian filter used for smoothing. Observe how the ‘sharpness' of the edges is lost after the smoothing operation in this image processing project. The smoothing process can also be performed on the RGB image. However, a grayscale image has been used here for simplicity.

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Edge detection helps segment images to allow for data extraction. An edge in an image is essentially a discontinuity (or a sharp change) in the pixel intensity values of an image. You must have witnessed edge detection at play in software like Kingsoft WPS or your own smartphone scanners and, therefore, should be familiar with its significance.

For this project, you can implement the Sobel operator for edge detection. For this, you can use OpenCV to read the image, NumPy to create the masks, perform the convolution operations, and combine the horizontal and vertical mask outputs to extract all the edges.

The above image demonstrates the results obtained by applying the Sobel filter to the smoothed image. 

NOTE: On comparing this to the results obtained by applying the Sobel filter directly to the Grayscaled image (without smoothing) as shown below, you should be able to understand one of the reasons why smoothing is essential before edge detection.

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Skew correction is beneficial in applications like OCR . The pain of skew correction is entirely avoided by having artificial intelligence -enabled features built into applications like Kingsoft WPS.

You can try using OpenCV to read and grayscale the image to implement your skew correction program. To eliminate the skew, you will need to compute the bounding box containing the text and adjust its angle. An example of the results of the skew correction operation has been shown. You can try to replicate the results by using this Kaggle dataset ImageProcessing . 

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Quoting Stephen Hawking, “A picture is worth a thousand words...and uses up a thousand times the memory.” Despite the advantages images have over text data, there is no denying the complexities that the extra bytes they eat up can bring. Optimization, therefore, becomes the only way out.

If words alone haven't made the case convincing enough, perhaps the mention of the startup, Deep Render, which is based on applying machine learning to image compression, raising £1.6 million in seed funding, should serve to emphasize the importance of this domain succinctly.

For this MATLAB Image Processing project, you can implement the discrete cosine transform approach to achieve image compression. It is based on the property that most of the critical information of an image can be described by just a few coefficients of the DCT. You can use the Image Processing Toolbox software for DCT computation. The input image is divided into 8-by-8 or 16-by-16 blocks, and the DCT coefficients computed, which have values close to zero, can be discarded without seriously degrading image quality. You can use the standard ‘cameraman.tif' image as input for this purpose. 

Image Credit: Mathworks.in 

Intermediate Image Processing Projects Ideas

In the previous section, we introduced simple image processing projects for beginners. We will now move ahead with projects on image processing that are slightly more difficult but equally interesting to attempt.

You must have come across several off-the-shelf software capable of cartooning and adding an artistic effect to your images. These features are enabled on popular social media platforms like Instagram and Snapchat. Producing images with effects of your liking is possible by using Neural Style Transfer.

To implement a model to achieve Neural Style Transfer, you need to choose a style image that will form the ‘effect' and a content image. (You can use this dataset: Tamil Neural Style Transfer Dataset   for this image processing project.) The feature portion of a pre-trained VGG-19 can be used for this purpose, and the weighted addition of the style and content loss function is to be optimized during backpropagation.

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Seldom would you find a smartphone or a camera without face detection still in use. This feature has become so mainstream that most major smartphone manufacturers, like Apple and Samsung, wouldn't explicitly mention its presence in product specifications.

For this project, you could choose one of the following two ways to implement face detection .

Image Processing Projects using OpenCV Python

For this approach, you could use the pre-trained classifier files for the Haar classifier. While this is not particularly hard to implement, there is much to learn from precisely understanding how the classifier works.

Deep Learning Approach

While dlib's CNN-based face detector is slower than most other methods, its superior performance compensates for its longer execution time. To implement this, you can simply use the pre-trained model from //dlib.net/files/mmod_human_face_detector.dat.bz2.

Irrespective of the approach you choose to go about with for the face detection task, you could use this Image Processing Random Faces Dataset on Kaggle.

Image-to-text conversion or Optical Character Recognition has been the basis of many popular applications such as Microsoft Office Lens and a feature of others such as Google documents. The prevalence of OCR systems is only rising as the world becomes increasingly digitized. Therefore, this digital image processing project will involve familiarizing yourself with accomplishing image-to-text conversion using MATLAB!

While converting image to text by Optical Character Recognition can be pretty easy with other programming languages like Python (for instance, using pyTesseract), the MATLAB implementation of this project can seem slightly unfamiliar. But unfamiliarity is all there is to this otherwise simple application. You can simply use the Computer Vision Toolbox to perform Optical Character Recognition. Additionally, you can use the pre-trained language data files in the OCR Language Data support files from the OCR Engine page, Tesseract Open Source OCR Engine. Further, as an extension of this project, you could try training your own OCR model using the OCR Trainer application for a specific set of characters, such as handwritten characters, mathematical characters, and so on.

Some publicly available datasets you could use for training on handwritten characters include Digits 0-9: MNIST , A-Z in CSV format , and Math symbols.

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Watermarking is a helpful way to protect proprietary images and data; it essentially signs your work the way painters do to their artwork. For digital watermarking to be effective, it should be imperceivable to the human eye and robust to withstand image manipulations like warping, rotation, etc.

For this project, you can combine Discrete Cosine Transform and Discrete Wavelet Transform for watermarking. You can implement an effective machine learning algorithm for watermarking by changing the wavelet coefficients of select DWT sub-bands followed by the application of DCT transform. Operations like DCT can be accomplished in Python Data Science Tutorial using the scipy library.

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Image Classification finds wide digital applications (for instance, it is used by Airbnb for room type classification) and industrial settings such as assembly lines to find faults, etc. 

One way to achieve image classification with MATLAB using the Computer Vision Toolbox function is by employing a visual bag of words. This involves extracting feature descriptors from the training data to train the classifier. The training essentially consists of converting the extracted features into an approximated feature histogram based on the likeness or closeness of the descriptors using clustering to arrive at the image's feature vector. This classifier is then used for prediction. To start this task, you could use this (adorable!) cat, dog, and panda classifier dataset .

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Advanced Python Image Processing Projects with Source Code

It is time to level up your game in image processing. After working on the above mentioned projects, we suggest you try out the following digital image processing projects using Python .

Background subtraction is an important technique used in applications like video surveillance. As video analysis involves frame-wise processing, this technique naturally finds its place in the image processing domain. It essentially separates the elements in the foreground from the background by generating a mask. 

To model the background, you will need to initialize a model to represent the background characteristics and update it to represent the changes such as lighting during different times of the day or the change in seasons. The Gaussian Mixture Model is one of the many algorithms you can use for this image processing project. (Alternatively, you can use the OpenCV library, which has some High-level APIs which will significantly simplify the task. However, it is advised to understand their working before using them.) The dataset made available on: http://pione.dinf.usherbrooke.ca/   can be used with due acknowledgments.

While object detection involves finding the location of an object and creating bounding boxes, instance segmentation goes a step beyond by identifying the exact pixels representing an instance of every class of object that the machine learning model has been trained. Instance segmentation finds its use in numerous state-of-the-art use cases like self-driving cars.

It is advised to use Mask RCNN for this image segmentation problem. You can use the pre-trained mask_rcnn_coco.h5 model and then provide an annotated dataset. The following miniature traffic dataset is annotated in COCO format and should aid transfer learning .

Source Code: Image Segmentation using Mask RCNN Data Science Project

Pose estimation finds use in augmented reality, robotics, and even gaming. The computer vision or deep learning-based company, Wrnch, is based on a product designed to estimate human pose and motion and reconstruct human shape digitally as two or three-dimensional characters.

Using the Open Pose algorithm, you can implement pose estimation with the Deep Learning Toolbox in MATLAB. The pre-trained model can be used from //ssd.mathworks.com/supportfiles/vision/data/human-pose-estimation.zip. (For this project, you can use the MPII Human Pose Dataset (human-pose.mpi-inf.mpg.de/).

Image processing in the medical field is a topic whose benefits and scopes need no introduction. Healthcare product giants like Siemens Healthineers and GE Healthcare have already headed into this domain by introducing AI-Rad Companion and AIRx (or Artificial Intelligence Prescription), respectively.

To accomplish the medical image segmentation task, you can consider implementing the famous U-Net architecture; a convolutional neural network developed to segment biomedical images using the Tensorflow API. While there have been many advancements and developments since U-Net, the popularity of this architecture and the possible availability of pre-trained models will perhaps help you get started. 

Using the two chest x-rays datasets from Montgomery County and Shenzhen Hospital, you can attempt lung image segmentation: hncbc.nlm.nih.gov/LHC-downloads/dataset.html. Alternatively, you can use the masks for the Shenzhen Hospital dataset .  

Source Code: Medical Image Segmentation

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IImage fusion combines or fuses multiple input images to obtain new or more precise knowledge. In medical image fusion, combining multiple images from single or multiple modalities reduces the redundancy and augments the usefulness and capabilities of medical images in diagnosis. Many companies (like ImFusion) provide expert services, and others develop in-house solutions for their image processing use cases, such as image fusion.

For your image fusion project, you can consider Multifocus Image Fusion.You can use the method described by Slavica Savić's paper titled “Multifocus Image Fusion Based on Empirical Mode Decomposition” for this. The following publicly available multi-focus image datasets can be used to build and evaluate the solution:  github.com/sametaymaz/Multi-focus-Image-Fusion-Dataset.

Image-Processing Projects using Python with Source Code on GitHub

This section is particularly for those readers who want solved projects on image processing using Python. We have mentioned the GitHub repository for each project so that you can understand the implementation of the projects deeply.

During summer holidays in childhood, most of us would have at least once tried playing the exciting game- Sudoku. It is common to get stuck when playing the game for the first time. But, the most challenging part is to go back and realize where one went wrong?

Well, if you are into image processing, you can build a project to solve the sudoku.

The project idea is to build an intelligent sudoku solver that can pull out the sudoku game from an input image and solve it. And the first step in creating such an application is to apply a gaussian blur filter to the image. After that, use adaptive gaussian thresholding, invert the colors, dilate the image, and use a convolutional neural network to recognize the puzzle. The last step is then to use mathematical algorithms to solve the puzzle.

FUN FACT: Sudoku is an abbreviation for “suuji wa dokushin ni kagiru” (japanese), which means “the numbers must remain single.” 

Source Code on GitHub: GitHub Repository:neeru1207/AI_Sudoku

This is one of the fun digital image processing projects you should try. It will introduce you to exciting and intriguing image processing techniques while guiding you on building a system that can detect bar codes from an image.

The idea behind this project is simple. One must make a computer locate that area in an image with maximum contours. You will first need to convert the image into grayscale to get started. After that, use image processing methods like gradient calculation, blur image, binary thresholding, morphology, etc., and finally find the area with the highest number of contours. Then, label the area as a bar code. Additionally, you can use OpenCV to decode the barcode. Source Code on GitHub: pyxploiter/Barcode-Detection-and-Decoding

Instagram is one of the top 6 social networks with more than a billion users, and we hope you are not surprised. It's the era of social media platforms, and photographs/digital images are one of the best ways to convey what's going on in your life. And, where there are images, there are filters to beautify them. In this project, you will build a system that can automatically correct the exposure of an input image. This project will help you understand image processing techniques like Histogram Equalisation, Bi-Histogram based Histogram Equalisation, Contrast Limited Adaptive Histogram Equalisation, Gamma Transformation, Adaptive Gamma Transformation, Weighted Adaptive Gamma Transformation, Improved Adaptive Gamma Transformation, and Adaptive non-linear Stretching.

Source Code on GitHub: GitHub - 07Agarg/Automatic-Exposure-Correction

While creating images on platforms like Canva, one often comes across images with great texture but do not have a high resolution. Well, through image processing techniques, you can easily create a solution for such images.

This project will create an application that will take a textured image as input and extend that texture to form a higher resolution image. Additionally, you will use the texture and overlap it over another image, referred to as image quilting. This project will use various image processing methods to pick the right texture and create the desired images. You will understand how different mathematical functions like root-mean-square are utilized over pixels for images.

Source Code on GitHub: GitHub - ani8897/Image-Quilting-and-Texture-Synthesis

Can you recall those childhood days when you'd request your siblings to sign your leave application on your parents' behalf by forging their signatures? Forging signatures sounds like a funny thing when you are a kid but not as an adult. Someone can withdraw money from your bank account without you knowing it by forging your signature. So, how do banks make sure it's you and nobody else? You guessed it right; they use image processing.

In this project, you evaluate the score difference between two images of signatures; one would be the original, and the other would be the test image. You will learn how to apply deep learning models like CNN, SigNet, etc., on processed images to build the signature verification application.

Source Code on GitHub: GitHub - DefUs3r/Automatic-Signature-Verification

Concluding with a quote from George Bernard Shaw, “The only way to learn something is to do something.” While (digital) image processing and machine learning were long established in his time, it doesn't make his advice any less applicable. Projects, short and fun as they are, are a great way to improve your skills in any domain. So, if you've made it up to here, make sure you don't leave without taking up an image processing project or two, and before you know it, you'll have the skills and the project portfolio to show for it!

FAQs on Image Processing Projects

Image processing is a method for applying operations on an image to enhance or extract relevant information. It's a form of signal processing in which the input is an image, and the output is either that image or its features. Example: Grayscaling is a popular image processing technique that reduces computational complexity while minimizing dimensionality.

Image processing has various applications such as Pattern Recognition , Video processing, Machine/Robot Vision, Image sharpening and restoration, Color processing, Microscopic Imaging, etc.

Here is a list of the best software for image processing:

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What is Digital Image Processing?

Digital image processing is the process of using computer algorithms to perform image processing on digital images. Latest topics in digital image processing for research and thesis are based on these algorithms. Being a subcategory of digital signal processing, digital image processing is better and carries many advantages over analog image processing. It permits to apply multiple algorithms to the input data and does not cause the problems such as the build-up of noise and signal distortion while processing. As images are defined over two or more dimensions that make digital image processing “a model of multidimensional systems”. The history of digital image processing dates back to early 1920s when the first application of digital image processing came into news. Many students are going for this field for their  m tech thesis  as well as for Ph.D. thesis. There are various thesis topics in digital image processing for M.Tech, M.Phil and Ph.D. students. The list of thesis topics in image processing is listed here. Before going into  topics in image processing , you should have some basic knowledge of image processing.

Latest research topics in image processing for research scholars:

• The hybrid classification scheme for plant disease detection in image processing
• The edge detection scheme in image processing using ant and bee colony optimization
• To improve PNLM filtering scheme to denoise MRI images
• The classification method for the brain tumor detection
• The CNN approach for the lung cancer detection in image processing
• The neural network method for the diabetic retinopathy detection
• The copy-move forgery detection approach using textual feature extraction method
• Design face spoof detection method based on eigen feature extraction and classification
• The classification and segmentation method for the number plate detection
• Find the link at the end to download the latest thesis and research topics in Digital Image Processing

Formation of Digital Images

Firstly, the image is captured by a camera using sunlight as the source of energy. For the acquisition of the image, a sensor array is used. These sensors sense the amount of light reflected by the object when light falls on that object. A continuous voltage signal is generated when the data is being sensed. The data collected is converted into a digital format to create digital images. For this process, sampling and quantization methods are applied. This will create a 2-dimensional array of numbers which will be a digital image.

Why is Image Processing Required?

• Image Processing serves the following main purpose:
• Visualization of the hidden objects in the image.
• Enhancement of the image through sharpening and restoration.
• Seek valuable information from the images.
• Measuring different patterns of objects in the image.
• Distinguishing different objects in the image.

Applications of Digital Image Processing

• There are various applications of digital image processing which can also be a good topic for the thesis in image processing. Following are the main applications of image processing:
• Image Processing is used to enhance the image quality through techniques like image sharpening and restoration. The images can be altered to achieve the desired results.
• Digital Image Processing finds its application in the medical field for gamma-ray imaging, PET Scan, X-ray imaging, UV imaging.
• It is used for transmission and encoding.
• It is used in color processing in which processing of colored images is done using different color spaces.
• Image Processing finds its application in machine learning for pattern recognition.

List of topics in image processing for thesis and research

• There are various in digital image processing for thesis and research. Here is the list of latest thesis and research topics in digital image processing:
• Image Acquisition
• Image Enhancement
• Image Restoration
• Color Image Processing
• Wavelets and Multi Resolution Processing
• Compression
• Morphological Processing
• Segmentation
• Representation and Description
• Object recognition
• Knowledge Base

1. Image Acquisition:

Image Acquisition is the first and important step of the digital image of processing . Its style is very simple just like being given an image which is already in digital form and it involves preprocessing such as scaling etc. It starts with the capturing of an image by the sensor (such as a monochrome or color TV camera) and digitized. In case, the output of the camera or sensor is not in digital form then an analog-to-digital converter (ADC) digitizes it. If the image is not properly acquired, then you will not be able to achieve tasks that you want to. Customized hardware is used for advanced image acquisition techniques and methods. 3D image acquisition is one such advanced method image acquisition method. Students can go for this method for their master’s thesis and research.

2. Image Enhancement:

Image enhancement is one of the easiest and the most important areas of digital image processing. The core idea behind image enhancement is to find out information that is obscured or to highlight specific features according to the requirements of an image. Such as changing brightness & contrast etc. Basically, it involves manipulation of an image to get the desired image than original for specific applications. Many algorithms have been designed for the purpose of image enhancement in image processing to change an image’s contrast, brightness, and various other such things. Image Enhancement aims to change the human perception of the images. Image Enhancement techniques are of two types: Spatial domain and Frequency domain.

3. Image Restoration:

Image restoration involves improving the appearance of an image. In comparison to image enhancement which is subjective, image restoration is completely objective which makes the sense that restoration techniques are based on probabilistic or mathematical models of image degradation. Image restoration removes any form of a blur, noise from images to produce a clean and original image. It can be a good choice for the M.Tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from the image enhancement method. Deconvolution technique is used and is performed in the frequency domain. The main defects that degrade an image are restored here.

4. Color Image Processing:

Color image processing has been proved to be of great interest because of the significant increase in the use of digital images on the Internet. It includes color modeling and processing in a digital domain etc. There are various color models which are used to specify a color using a 3D coordinate system. These models are RGB Model, CMY Model, HSI Model, YIQ Model. The color image processing is done as humans can perceive thousands of colors. There are two areas of color image processing full-color processing and pseudo color processing. In full-color processing, the image is processed in full colors while in pseudo color processing the grayscale images are converted to colored images. It is an interesting topic in image processing.

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Jorge valente.

1 Techframe-Information Systems, SA, 2785-338 São Domingos de Rana, Portugal; [email protected] (J.V.); [email protected] (J.A.)

João António

Carlos mora.

2 Smart Cities Research Center, Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal; [email protected]

Sandra Jardim

Associated data.

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to company privacy matters; however, all data contained in the dataset mentioned in the manuscript is publicly available.

The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

1. Introduction

Images constitute one of the most important forms of communication used by society and contain a large amount of important information. The human vision system is usually the first form of contact with media and has the ability to naturally extract important, and sometimes subtle, information, enabling the execution of different tasks, from the simplest, such as identifying objects, to the more complex, such as the creation and integration of knowledge. However, this system is limited to the visible range of the electromagnetic spectrum. On the contrary, computer systems have a more comprehensive coverage capacity, ranging from gamma to radio waves, which makes it possible to process a wide spectrum of images, covering a wide and varied field of applications. On the other hand, the exponential growth in the volume of images created and stored daily makes their analysis and processing a difficult task to implement outside the technological sphere. In this way, image processing through computational systems plays a fundamental role in extracting necessary and relevant information for carrying out different tasks in different contexts and application areas.

Image processing originated in 1964 with the processing of the images of the lunar surface, and in a simple way, we can define the concept of image processing as an area of signal processing dedicated to the development of computational techniques aimed at the analysis, improvement, compression, restoration, and extraction of information from digital images. With a wide range of applications, image processing has been a subject of great interest both from the scientific community and from industry. This interest, combined with the technological evolution of computer systems and the need to have systems with increasingly better levels of performance, both in terms of precision and reliability and in terms of processing speed, has enabled a great evolution of image processing techniques, moving from the use of nonlearning-based methods to the application of machine learning techniques.

Having emerged in the mid-twentieth century, machine learning (ML) is a subset of artificial intelligence (AI), a field of computer science that focuses on designing machines and computational solutions capable of executing, ideally automatically, tasks that include, among others, natural language understanding, speech understanding, and image recognition [ 1 ]. When providing new ways to design AI models [ 2 ], ML, such as other scientific computing applications, commonly uses linear algebra operations on multidimensional arrays, which are computational data structures for representing vectors, matrices, and tensors of a higher order. ML is a data analysis method that automates the construction of analytical models and computer algorithms, which are used in a large range of data types [ 1 ] and are particularly useful for analyzing data and establishing potential patterns to try and predict new information [ 3 ]. This suit of techniques has exploded in use and as a topic of research over the past decade, to the point where almost everyone interacts with modern AI models many times every day [ 4 ].

AI, in particular ML, has revolutionized many areas of technology. One of the areas where the impact of such techniques is noticeable is image processing. The advancement of algorithms and computational capabilities has driven and enabled the performance of complex tasks in the field of image processing, such as facial recognition, object detection and classification, generation of synthetic images, semantic segmentation, image restoration, and image retrieval. The application of ML techniques in image processing brings a set of benefits that impact different sectors of society. This technology has the potential to optimize processes, improve the accuracy of data analysis, and provide new possibilities in different areas. With ML techniques, it is possible to analyze and interpret images with high precision. The advances that have been made in the use of neural networks have made it possible to identify objects, recognize patterns, and carry out complex analyses on images with a high accuracy rate. Pursuing ever-increasing precision is essential in areas such as medicine, where accurate diagnosis can make a difference in patients’ lives.

By applying ML techniques and models to image processing, it is possible to automate tasks that were previously performed manually. In this context, and as an example, we have quality control processes in production lines, where ML allows for the identification of defects in products quickly and accurately, eliminating the need for human inspection, leading to an increase in process efficiency, as well as to a reduction in errors inherent to the human factor and costs.

The recognized ability of ML models to extract valuable information from images enables advanced analysis in several areas, namely public safety, where facial recognition algorithms can be used to identify individuals, and scientific research, such as the inspection of astronomical images, the classification of tissues or tumor cells, and the detection of patterns in large volumes of data.

With so much new research and proposed approaches being published with high frequency, it is a daunting task to keep up with current trends and new research topics, especially if they occur in a research field one is not familiar with. For this purpose, we propose to explore and review publications discussing the new techniques available and the current challenges and point out some of the possible directions for the future. We believe this research can prove helpful for future researchers and provide a modern vision of this fascinating and vast research subject.

On the other hand, and as far as it was possible to verify from the analysis of works published in recent years, there is a lack of studies that highlight machine learning techniques applied to image processing in different areas. There are several works that focus on reviewing the work that has been developed in a given area, and the one that seems to arouse the most interest is the area of medical imaging [ 5 , 6 , 7 , 8 , 9 ]. Therefore, this paper also contributes to presenting an analysis and discussion of ML techniques in a broad context of application.

This document is divided into sections, subsections, and details. Section 2 —Introduction: describes the research methodology used to carry out this review manuscript. Section 3 —Technical Background: presents an overview of the AI models most used in image processing. Section 4 —Image Processing Developments: describes related work and different state-of-the-art approaches used by researchers to solve modern-day challenges. Section 5 —Discussion and Future Directions: presents the main challenges and limitations that still exist in the area covered by this manuscript, pointing out some possible directions for the evolution of the models proposed to date. Finally, Section 6 provides a brief concluding remark with the main conclusions that can be taken from our study.

2. Methodology

In order to carry out this review, we considered a vast number of scientific publications in the scope of ML, particularly those involving image processing methods using DL and RL techniques and applied to real-world problems.

2.1. Search Process and Sources of Information

In order to guarantee the reliability of the documents, the information sources were validated, having been considered reputable publication journals and university repositories. From the selected sources, we attempted to include research from multiple areas and topics to provide a general and detailed representation of the ways image processing research has developed and can be used. Nevertheless, some areas appear to have developed a greater interest in some of the ML methods previously described. The search process involved using a selection of keywords that are closely related to image processing on popular scientific search engines such as Springer Science Direct, and Core. These search engines were selected since they allowed us to make comparable searches, targeting specific terms and filtering results by research area. In order to cover a broad range of topics and magazines, the only search filter that we used was chosen to ensure that the subjects were related to data science and/or artificial intelligence.

As of February 2023, a search using the prompt “image processing AI” returns manuscripts related mostly to “Medicine”, “Computer science”, and “Engineering”. In fact, while searching in the three different research aggregators, the results stayed somewhat consistent. A summary of the results obtained can be observed in Figure 1 .

Main research areas for the tested search inputs for three different academic engines.

Since there is more research available on some topics, the cases described ahead can also have a higher prevalence when compared to others.

2.2. Inclusion and Exclusion Criteria for Article Selection

The research carried out in the different repositories resulted in a large number of research works proposed by different authors. By considering the constant advances made in this subject and the amount of research developed, we opted to mainly focus on research developed in the last 5 years. We analyzed and selected the research sources that provided novel and/or interesting applications of ML in image processing. The objective was to present a broad representation of the recent trends in ML research and provide the information in a more concise form.

3. Technical Background

The growing use of the internet in general, and social networks in particular, has led to the availability of a large increase in digital images; being privileged means being able to express emotions and share information, which enables many diverse applications [ 10 ]. Identifying the interesting parts of a scene is a fundamental step for recognizing and interpreting an image [ 11 ]. To understand how the different techniques are applied in processing images and extracting their features, as well as explaining the main concepts and technicalities of the different types of AI models, we will provide a general technical background review of machine learning and image processing, which will help provide relevant context to the scope of this review, as well as guide the reader through the covered topics.

3.1. Graphics Processing Units

Many advances covered in this paper, along with classical ML and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of AI [ 1 , 10 ], enabling the processing of massive amounts of data generated each day and lowering the barrier to adoption [ 1 ]. In particular, the usage of GPUs revolutionized the landscape of classical ML and DL models. From the 1990s to the late 2000s, ML research was predominantly focused on SVM, which was considered state-of-the-art [ 1 ]. In the following decade, starting in 2010, GPUs brought new life into the field of DL, jumpstarting a high amount of research and development [ 1 ]. State-of-the-art DL algorithms tend to have higher computational complexity, requiring several iterations to make the parameters converge to an optimal value [ 12 , 13 ]. However, the relevance of DL has only become greater over the years, as this technology has gradually become one of the main focuses of ML research [ 14 ].

While research into the use of ML on GPUs predates the recent resurgence of DL, the usage of general-purpose GPUs for computing (GPGPU) became widespread when CUDA was released in 2007 [ 1 ]. Shortly after, CNN started to be implemented on top of GPUs, demonstrating dramatic end-to-end speedup, even over highly optimized CPU implementations. CNNs are a subset of methods that can be used, for example, for image restoration, which has demonstrated outstanding performance [ 1 ]. Some studies have shown that, when compared with traditional neural networks and SVM, the accuracy of recognition using CNNs is notably higher [ 12 ].

Some of these performance gains were accomplished even before the existence of dedicated GPU-accelerated BLAS libraries. The release of the first CUDA Toolkit brought new life to general-purpose parallel computing with GPUs, with one of the main benefits of this approach being the ability of GPUs to enable a multithreaded single-instruction (SIMT) programming paradigm, higher throughput, and more parallel models when compared to SIMD. This process makes several blocks of multiprocessors available, each with many parallel cores (threads), allowing access to high-speed memory [ 1 ].

3.2. Image Processing

For humans, an image is a visual and meaningful arrangement of regions and objects [ 11 ]. Recent advances in image processing methods find application in different contexts of our daily lives, both as citizens and in the professional field, such as compression, enhancement, and noise removal from images [ 10 , 15 ]. In classification tasks, an image can be transformed into millions of pixels, which makes data processing very difficult [ 2 ]. As a complex and difficult image-processing task, segmentation has high importance and application in several areas, namely in automatic visual systems, where precision affects not only the segmentation results but also the results of the following tasks, which, directly or indirectly, depend on it [ 11 ]. In segmentation, the goal is to divide an image into its constituent parts (or objects)—sometimes referred to as regions of interest (ROI)—without overlapping [ 16 , 17 ], which can be achieved through different feature descriptors, such as the texture, color, and edges, as well as a histogram of oriented gradients (HOG) and a global image descriptor (GIST) [ 11 , 17 ]. While the human vision system segments images on a natural basis, without special effort, automatic segmentation is one of the most complex tasks in image processing and computer vision [ 16 ].

Given its high applicability and importance, object detection has been a subject of high interest in the scientific community. Depending on the objective, it may be necessary to detect objects with a significant size compared to the image where they are located or to detect several objects of different sizes. The results of object detection in images vary depending on their dimensions and are generally better for large objects [ 18 ]. Image processing techniques and algorithms find application in the most diverse areas. In the medical field, image processing has grown in many directions, including computer vision, pattern recognition, image mining, and ML [ 19 ].

In order to use some ML models when problems in image processing occur, it is often necessary to reduce the number of data entries to quickly extract valuable information from the data [ 10 ]. In order to facilitate this process, the image can be transformed into a reduced set of features in an operation that selects and measures the representative data properties in a reduced form, representing the original data up to a certain degree of precision, and mimicking the high-level features of the source [ 2 ]. While deep neural networks (DNNs) are often used for processing images, some traditional ML techniques can be applied to improve the data obtained. For example, in Zeng et al. [ 20 ], a deep convolutional neural network (CNN) was used to extract image features, and principal component analysis (PCA) was applied to reduce the dimensionality of the data.

3.3. Machine Learning Overview

ML draws inspiration from a conceptual understanding of how the human brain works, focusing on performing specific tasks that often involve pattern recognition, including image processing [ 1 ], targeted marketing, guiding business decisions, or finding anomalies in business processes [ 4 ]. Its flexibility has allowed it to be used in many fields owing to its high precision, flexible customization, and excellent adaptability, being increasingly more common in the fields of environmental science and engineering, especially in recent years [ 3 ]. When learning from data, deep learning systems acquire the ability to identify and classify patterns, making decisions with minimal human intervention [ 2 ]. Classical techniques are still fairly widespread across different research fields and industries, particularly when working with datasets not appropriate for modern deep learning (DL) methods and architectures [ 1 ]. In fact, some data scientists like to reinforce that no single ML algorithm fits all data, with proper model selection being dependent on the problem being solved [ 21 , 22 ]. In diagnosis modeling that uses the classification paradigm, the learning process is based on observing data as examples. In these situations, the model is constructed by learning from data along with its annotated labels [ 2 ].

While ML models are an important part of data handling, other steps need to be taken in preparation, like data acquisition, the selection of the appropriate algorithm, model training, and model validation [ 3 ]. The selection of relevant features is one of the key prerequisites to designing an efficient classifier, which allows for robust and focused learning models [ 23 ].

There are two main classes of methods in ML: supervised and unsupervised learning, with the primary difference being the presence of labels in the datasets.

• In supervised learning, we can determine predictive functions using labeled training datasets, meaning each data object instance must include an input for both the values and the expected labels or output values [ 21 ]. This class of algorithms tries to identify the relationships between input and output values and generate a predictive model able to determine the result based only on the corresponding input data [ 3 , 21 ]. Supervised learning methods are suitable for regression and data classification, being primarily used for a variety of algorithms like linear regression, artificial neural networks (ANNs), decision trees (DTs), support vector machines (SVMs), k-nearest neighbors (KNNs), random forest (RF), and others [ 3 ]. As an example, systems using RF and DT algorithms have developed a huge impact on areas such as computational biology and disease prediction, while SVM has also been used to study drug–target interactions and to predict several life-threatening diseases, such as cancer or diabetes [ 23 ].
• Unsupervised learning is typically used to solve several problems in pattern recognition based on unlabeled training datasets. Unsupervised learning algorithms are able to classify the training data into different categories according to their different characteristics [ 21 , 24 ], mainly based on clustering algorithms [ 24 ]. The number of categories is unknown, and the meaning of each category is unclear; therefore, unsupervised learning is usually used for classification problems and for association mining. Some commonly employed algorithms include K-means [ 3 ], SVM, or DT classifiers. Data processing tools like PCA, which is used for dimensionality reduction, are often necessary prerequisites before attempting to cluster a set of data.

Some studies make reference to semi-supervised learning, in which a combination of unsupervised and supervised learning methods are used. In theory, a mixture of labeled and unlabeled data is used to help reduce the costs of labeling a large amount of data. The advantage is that the existence of some labeled data should make these models perform better than strictly unsupervised learning [ 21 ].

In addition to the previously mentioned classes of methods, reinforcement learning (RL) can also be regarded as another class of machine learning (ML) algorithms. This class refers to the generalization ability of a machine to correctly answer unlearned problems [ 3 ].

The current availability of large amounts of data has revolutionized data processing and statistical modeling techniques but, in turn, has brought new theoretical and computational challenges. Some problems have complex solutions due to scale, high dimensions, or other factors, which might require the application of multiple ML models [ 4 ] and large datasets [ 25 ]. ML has also drawn attention as a tool in resource management to dynamically manage resource scaling. It can provide data-driven methods for future insights and has been regarded as a promising approach for predicting workload quickly and accurately [ 26 ]. As an example, ML applications in biological fields are growing rapidly in several areas, such as genome annotation, protein binding, and recognizing the key factors of cancer disease prediction [ 23 ]. The deployment of ML algorithms on cloud servers has also offered opportunities for more efficient resource management [ 26 ].

Most classical ML techniques were developed to target structured data, meaning data in a tabular form with data objects stored as rows and the features stored as columns. In contrast, DL is specifically useful when working with larger, unstructured datasets, such as text and images [ 1 ]. Additional hindrances may apply in certain situations, as, for example, in some engineering design applications, heterogeneous data sources can lead to sparsity in the training data [ 25 ]. Since modern problems often require libraries that can scale for larger data sizes, a handful of ML algorithms can be parallelized through multiprocessing. Nevertheless, the final scale of these algorithms is still limited by the amount of memory and number of processing cores available on a single machine [ 1 ].

Some of the limitations in using ML algorithms come from the size and quality of the data. Real datasets are a challenge for ML algorithms since the user may face skewed label distributions [ 1 ]. Such class imbalances can lead to strong predictive biases, as models can optimize the training objective by learning to predict the majority label most of the time. The term “ensemble techniques” in ML is used for combinations of multiple ML algorithms or models. These are known and widely used for providing stability, increasing model performance, and controlling the bias-variance trade-off [ 1 ]. Hyperparameter tuning is also a fundamental use case in ML, which requires the training and testing of a model over many different configurations to be able to find the model with the best predictive performance. The ability to train multiple smaller models in parallel, especially in a distributed environment, becomes important when multiple models are being combined [ 1 ].

Over the past few years, frequent advances have occurred in AI research caused by a resurgence in neural network methods that have fueled breakthroughs in areas like image understanding, natural language processing, and others [ 27 ]. One area of AI research that appears particularly inviting from this perspective is deep reinforcement learning (DRL), which marries neural network modeling with RL techniques. This technique has exploded within the last 5 years into one of the most intense areas of AI research, generating very promising results to mimic human-level performance in tasks varying from playing poker [ 28 ], video games [ 29 ], multiplayer contests, and complex board games, including Go and Chess [ 27 ]. Beyond its inherent interest as an AI topic, DRL might hold special interest for research in psychology and neuroscience since the mechanisms that drive learning in DRL were partly inspired by animal conditioning research and are believed to relate closely to neural mechanisms for reward-based learning centering on dopamine [ 27 ].

3.3.1. Deep Learning Concepts

DL is a heuristic learning framework and a sub-area of ML that involves learning patterns in data structures using neural networks with many nodes of artificial neurons called perceptrons [ 10 , 19 , 30 ] (see Figure 2 ). Artificial neurons can take several inputs and work according to a mathematical calculation, returning a result in a process similar to a biological neuron [ 19 ]. The simplest neural network, known as a single-layer perceptron [ 30 ], is composed of at least one input, one output, and a processor [ 31 ]. Three different types of DL algorithms can be differentiated: multilayered perceptron (MLP) with more than one hidden layer, CNN, and recurrent neural networks (RNNs) [ 32 ].

Differences in the progress stages between traditional ML methods and DL methods.

One important consideration towards generic neural networks is they are extremely low-bias learning systems. As dictated by the bias–variance trade-off, this means that neural networks, in the most generic form employed in the first DRL models, tend to be sample-inefficient and require large amounts of data to learn. A narrow hypothesis set can speed the learning process if it contains the correct hypothesis or if the specific biases the learner adopts happen to fit with the material to be learned [ 27 ]. Several proposals for algorithms and models have emerged, some of which have been extensively used in different contexts, such as CNNs, autoencoders, and multilayer feedback RNN [ 10 ]. For datasets of images, speech, and text, among others, it is necessary to use different network models in order to maximize system performance [ 33 ]. DL models are often used for image feature extraction and recognition, given their higher performance when dealing with some of the traditional ML problems [ 10 ].

DL techniques differ from traditional ML in some notable ways (see also Figure 2 ):

• Training a DNN implies the definition of a loss function, which is responsible for calculating the error made in the process given by the difference between the expected output value and that produced by the network. One of the most used loss functions in regression problems is the mean squared error (MSE) [ 30 ]. In the training phase, the weight vector that minimizes the loss function is adjusted, meaning it is not possible to obtain analytical solutions effectively. The loss function minimization method usually used is gradient descent [ 30 ].
• Activation functions are fundamental in the process of learning neural network models, as well as in the interpretation of complex nonlinear functions. The activation function adds nonlinear features to the model, allowing it to represent more than one linear function, which would not happen otherwise, no matter how many layers it had. The Sigmoid function is the most commonly used activation function in the early stages of studying neural networks [ 30 ].
• As their capacity to learn and adjust to data is greater than that of traditional ML models, it is more likely that overfitting situations will occur in DL models. For this reason, regularization represents a crucial and highly effective set of techniques used to reduce the generalization errors in ML. Some other techniques that can contribute to achieving this goal are increasing the size of the training dataset, stopping at an early point in the training phase, or randomly discarding a portion of the output of neurons during the training phase [ 30 ].
• In order to increase stability and reduce convergence times in DL algorithms, optimizers are used, with which greater efficiency in the hyperparameter adjustment process is also possible [ 30 ].

In the last decades, three main mathematical tools have been studied for image modeling and representation, mainly because of their proven modeling flexibility and adaptability. These methods are the ones based on probability statistics, wavelet analysis, and partial differential equations [ 34 , 35 ]. In image processing procedures, it is sometimes necessary to reduce the number of input data. An image can be translated into millions of pixels for tasks, such as classifications, meaning that data entry would make the processing very difficult. In order to overcome some difficulties, the image can be transformed into a reduced set of features, selecting and measuring some representative properties of raw input data in a more reduced form [ 2 ]. Since DL technologies can automatically mine and analyze the data characteristics of labeled data [ 13 , 14 ], this makes DL very suitable for image processing and segmentation applications [ 14 ]. Several approaches use autoencoders, a set of unsupervised algorithms, for feature selection and data dimensionality reduction [ 31 ].

Among the many DL models, CNNs have been widely used in image processing problems, proving more powerful capabilities in image processing than traditional algorithms [ 36 ]. As shown in Figure 3 , a CNN, like a typical neural network, comprises an input layer, an output layer, and several hidden layers [ 37 ]. A single hidden layer in a CNN typically consists of a convolutional layer, a pooling layer, a fully connected layer [ 38 ], and a normalization layer.

Illustration of the structure of a CNN.

Additionally, the number of image-processing applications based on CNNs is also increasing daily [ 10 ]. Among the different DL structures, CNNs have proven to be more efficient in image recognition problems [ 20 ]. On the other hand, they can be used to improve image resolution, enhancing their applicability in real problems, such as the transmission or storage of images or videos [ 39 ].

DL models are frequently used in image segmentation and classification problems, as well as object recognition and image segmentation, and they have shown good results in natural language processing problems. As an example, face recognition applications have been extensively used in multiple real-life examples, such as airports and bank security and surveillance systems, as well as mobile phone functionalities [ 10 ].

There are several possible applications for image-processing techniques. There has been a fast development in terms of surveillance tools like CCTV cameras, making inspecting and analyzing footage more difficult for a human operator. Several studies show that human operators can miss a significant portion of the screen action after 20 to 40 minutes of intensive monitoring [ 18 ]. In fact, object detection has become a demanding study field in the last decade. The proliferation of high-powered computers and the availability of high-speed internet has allowed for new computer vision-based detection, which has been frequently used, for example, in human activity recognition [ 18 ], marine surveillance [ 40 ], pedestrian identification [ 18 ], and weapon detection [ 41 ].

One alternative application of ML in image-processing problems is image super-resolution (SR), a family of technologies that involve recovering a super-resolved image from a single image or a sequence of images of the same scene. ML applications have become the most mainstream topic in the single-image SR field, being effective at generating a high-resolution image from a single low-resolution input. The quality of training data and the computational demand remain the two major obstacles in this process [ 42 ].

3.3.2. Reinforcement Learning Concepts

RL is a set of ML algorithms that use a mathematical framework that can learn to optimize control strategies directly from the data [ 4 , 43 ] based on a reward function in a Markov decision process [ 44 , 45 ]. The Markov decision process (MDP) is a stochastic process used to model the decision-making process of a dynamic system. The decision process is sequential, where actions/decisions depend on the current state and the system environment, influencing not only the immediate rewards but also the entire decision process [ 4 ]. One commonly referenced RL problem is the multi-armed bandit , in which an agent selects one of n different options and receives a reward depending on the selection. This problem illustrates how RL can provide a trade-off between exploration (trying different arms) and exploitation (playing the arm with the best results) [ 44 ]. This group of algorithms is derived from behaviorist psychology, where an intelligent body explores the external environment and updates its strategy with feedback signals to maximize the cumulative reward [ 43 ], which means the action is exploitative [ 46 ].

In RL, the behavior of the Markov decision process is determined by a reward function [ 4 ]. The basis of a DRL network is made up of an agent and an environment, following an action-reward type of operation. The interaction begins in the environment with the sending of its state to the agent, which takes an action consistent with the state received, according to which it is subsequently rewarded or penalized by the environment [ 4 , 44 , 46 , 47 , 48 ]. RL is considered an autonomous learning technique that does not require labeled data but for which search and value function approximation are vital tools [ 4 ]. Often, the success of RL algorithms depends on a well-designed reward function [ 45 ]. Current RL methods still present some challenges, namely the efficiency of the learning data and the ability to generalize to new scenarios [ 49 ]. Nevertheless, this group of techniques has been used with tremendous theoretical and practical achievements in diverse research topics such as robotics, gaming, biological systems, autonomous driving, computer vision, healthcare, and others [ 44 , 48 , 50 , 51 , 52 , 53 ].

One common technique in RL is random exploration, where the agent makes a decision on what to do randomly, regardless of its progress [ 46 ]. This has become impractical in some real-world applications since learning times can often become very large. Recently, RL has shown a significant performance improvement compared to non-exploratory algorithms [ 46 , 54 ]. Another technique, inverse reinforcement learning (IRL), uses an opposite strategy by aiming to find a reward function that can explain the desired behavior [ 45 ]. In a recent study using IRL, Hwang et al. [ 45 ] proposed a new RL method, named option compatible reward inverse reinforcement learning , which applies an alternative framework to the compatible reward method. The purpose was to assign reward functions to a hierarchical IRL problem that is introduced while making the knowledge transfer easier by converting the information contained in the options into a numerical reward value. While the authors concluded that their novel algorithm was valid in several classical benchmark domains, they remarked that applying it to real-world problems still required extended evaluation.

RL models have been used in a wide variety of practical applications. For example, the COVID-19 pandemic was one of the health emergencies with the widest impact that humans have encountered in the past century. Many studies were directed towards this topic, including many that used ML techniques to several effects. Zong and Luo (2022) [ 55 ] conducted a study where they employed a custom epidemic simulation environment for COVID-19 where they applied a new multi-agent RL-based framework to explore optimal lockdown resource allocation strategies. The authors used real epidemic transmission data to calibrate the employed environment to obtain results more consistent with the real situation. Their results indicate that the proposed approach can adopt a flexible allocation strategy according to the age distribution of the population and economic conditions. These insights could be extremely valuable for decision-makers in supply chain management.

Some technical challenges blocked the combination of DNN with RL until 2015, when breakthrough research demonstrated how the integration could work in complex domains, such as Atari video games [ 29 , 56 ], leading to rapid progress toward improving and scaling DRL [ 27 ]. Some of the first successful applications of DRL came with the success of the deep Q network algorithm [ 56 ]. Currently, the application of DRL models to computer vision problems, such as object detection and tracking or image segmentation, has gained emphasis, given the good results it has produced [ 31 ]. RL, along with supervised and unsupervised methods, are the three main pattern recognition models used for research [ 57 ].

The initial advances in RL were boosted by the good performance of the [ 56 ] replay algorithm, as well as the use of two networks, one with fixed weights, which serves as the basis for a second network, for which the weights are iteratively updated during training, replacing the first one when the learning process ends. With the aim of reducing the high convergence times of DRL algorithms, several distributed framework approaches [ 58 ] have been proposed. This suit of methods has been successfully used for applications in computer vision [ 59 ] and in robotics [ 58 ].

3.4. Current Challenges

Considering everything that has been discussed previously, some of the main challenges that AI image processing faces are common across multiple subjects. Most applications require a large volume of images that are difficult to obtain. Indeed, due to the large amount of data, the process of extracting features from a dataset can become very time and resource-consuming. Some models, such as CNNs, can potentially have millions of parameters to be learned, which might require considerable effort to obtain sufficient labeled data [ 60 ]. Since AI models are heavily curated for a given purpose, the model obtained will likely be inapplicable outside of the specific domain in which it was trained. The performance of a model can be heavily impacted by the data available, meaning the accuracy of the outcome can also vary heavily [ 61 ]. An additional limitation that has been identified during research is the sensitivity of models regarding noisy or biased data [ 60 ]. A meticulous and properly designed data-collection plan is essential, often complemented by a prepossessing phase to ensure good-quality data. Some researchers have turned their attention to improving the understanding of the many models. Increased focus has been placed on the way the weights of a neural network can sometimes be difficult to decipher and extract useful information from, which can lead to wrong assumptions and decisions [ 62 ]. In order to facilitate communication and discussion, some authors have also attempted to provide a categorization system of DL methodologies based on their applications [ 31 ].

4. Image Processing Developments

The topic of ML has been studied with very broad applications and in multiple areas that require data collection and processing. Considering recent publications from the last 7 years (2017–2023), we see that several studies have been developed dealing with different subjects, with proposals of many different models. In particular, we found a considerable amount of research papers showing interest in using DL in medicine, engineering, and biology. When we consider the volume of research developed, there is a clear increase in published research papers targeting image processing and DL, over the last decades. A search using the terms “image processing deep learning” in Springerlink generated results demonstrating an increase from 1309 articles in 2005 to 30,905 articles in 2022, only considering review and research papers. In the aggregator Science Direct , we saw a similar result, demonstrating an increase from 1173 in 2005 to 27,393 scientific manuscripts in 2022. The full results across the referred timeline can be observed in Figure 4 . These results validate an upward trend in attention to DL methods, as also described in the previous section.

Number of research articles found using the search query “image processing deep learning” for two different aggregators.

A lot of recent literature, especially in the medical field, has attempted to address the biggest challenges, mainly derived from data scarcity and model performance [ 14 , 61 , 62 , 63 , 64 ]. Some research has focused on improving perforce or reducing the computational requirements in models such as CNNs [ 60 , 65 , 66 ] using techniques such as model pruning or compression. These have the objective of reducing the model’s overall size or operating cost. In the next section, we will discuss relevant approaches taken on the subject to illustrate how the scientific community has been using ML methods to solve specific data-driven problems and discuss some of the implications.

4.1. Domains

Studies involving image processing can be found on topics such as several infrastructure monitoring applications [ 13 , 67 , 68 ] in road pavement [ 69 , 70 , 71 ], remote sensing images [ 12 ], image reconstruction [ 72 ], detecting and quantifying plant diseases [ 73 , 74 , 75 , 76 , 77 ], identification of pests in plant crops [ 17 , 78 , 79 ], automated bank cheque verification [ 80 ] or even for graphical search [ 11 , 81 , 82 , 83 ]. There is also an ample amount of research using ML algorithms in the medical field. DL techniques have been applied in infection monitoring [ 64 , 84 , 85 ], in developing personalized advice for treatment [ 19 , 86 ], in diagnosing several diseases like COVID-19 [ 63 , 87 , 88 , 89 ], or imaging procedures including radiology [ 14 , 63 , 90 , 91 ] and pathology imaging [ 19 ] or in cancer screening [ 91 , 92 , 93 , 94 ].

While most modern research hasn’t focused on traditional ML techniques, there are still some valuable lessons to be taken from these studies, with interesting results obtained in engineering subjects. In 2022, Pratap and Sardana [ 21 ] conducted and published a review on image processing in materials science and engineering using ML. In this study, the authors reviewed several research materials focusing on ML, the ML model selection, and the image processing technique used, along with the context of the problem. The authors suggested SimpleCV as a possible framework, specifically for digital image processing. This type of approach was justified by the authors since materials have a 3D structure but most of the analysis on image processing that has been done is of 2D images [ 21 ]. Image super-resolution (SR) is another interesting application of ML concepts for image processing challenges that has attracted some attention in the past decades [ 15 , 42 ]. In 2016, Zhao et al. [ 42 ] proposed a framework for single-image super-resolution tasks, consisting of kernel blur estimation, to improve the training quality as well as the model performance. Using the kernel blur estimation, the authors adopted a selective patch processing strategy combined with sparse recovery. While their result indicated a better level of performance than several super-resolution approaches, some of the optimization problems encountered were, themselves, extraordinarily time-consuming, and as such, not a suitable solution for efficiency improvement. Research such as those can often serve as inspiration to address nuanced engineering problems that may be more specific to certain research subjects. As an example, in the last decade, the automobile industry has made a concerted shift towards intelligent vehicles equipped with driving assistance systems, with new vision systems in some higher-end cars. Some vision systems include cameras mounted in the car, which can be used by engineers to obtain large quantities of images and develop many of the future self-driving car functionalities [ 66 ].

Some advanced driver assistance systems (ADAS) that use AI have been proposed to assist drivers and attempt to significantly decrease the number of accidents. These systems often employ technologies such as image sensors, global positioning, radar imaging, and computer vision techniques. Studies have been developed that tested a number of different image processing techniques to understand their accuracy and limitations and found good results with traditional ML methods like SVM and optimum-path forest classifier [ 95 ] or K-Means clustering [ 11 ]. One potential benefit of using this approach is that some traditional methods can be less costly to apply and can be used as complementary on many different subjects. Rodellar et al. [ 16 ] investigated the existing research on the analysis of blood cells, using image processing. The authors acknowledged the existence of subtle morphological differences for some lymphoma and leukemia cell types, that are difficult to identify in routine screening. Some of their most curious findings were that the methods most commonly used in the classification of PB cells were Neural Networks, Decision Trees (DT), and SVM. The authors noted that image-based automatic recognition systems could position themselves as new modules of the existing analyzers or even that new systems could be built and combined with other well-established ones.

4.1.1. Research Using Deep Learning

Regarding Deep Learning methodologies, many studies attempt to improve the performance of DL models, which we highlight next. In their research, Monga et al. [ 96 ] conducted a review of usage and research involving Deep Neural Networks (DNN) that covered some of the most popular techniques for algorithm unrolling in several domains of signal and image processing. The authors extensively covered research developed on a technique called algorithm unrolling or unfolding. This method can provide a concrete and systematic connection between iterative algorithms, which are used widely in signal processing, and DNNs. This type of application has recently attracted enormous attention both in theoretical investigations and practical applications. The authors noted that while substantial progress has been made, more work needs to be done to comprehend the mechanism behind the unrolling network behavior. In particular, they highlight the need to clarify why some of the state-of-the-art networks perform so well on several recognition tasks. In a study published by Zeng et al. [ 20 ], a correction neural network model named Boundary Regulated Network (BR-Net) was proposed. It used high-resolution remote satellite images as the source, and the features of the image were extracted through convolution, pooling, and classification. The model accuracy was additionally increased through training on the experimental dataset in a particular area. In their findings, the authors indicated a performance improvement of 15%, while the recognition speed was also increased by 20%, compared with the newly researched models, further noting that, for a considerably large amount of data, the model will have poor generalization ability. In Farag [ 66 ], the investigation focused on the ability of a CNN model to learn safe driving maneuvers based on data collected using a front-facing camera. Their data collection happened using urban routes and was performed by an experienced driver. The author developed a 17-layer behavior cloning CNN model with four drop-out layers added to prevent overfitting during training. The results looked promising enough, whereby a small amount of training data from a few tracks was sufficient to train the car to drive safely on multiple tracks. For such an approach, one possible shortcoming is that the approach taken may require a massive number of tracks in order to be able to generalize correctly for actual street deployment.

Some modern research has focused on expanding the practical applications of DL models in image processing:

• One of the first DL models used for video prediction, inspired by the sequence-to-sequence model usually used in natural language processing [ 97 ], uses a recurrent long and short term memory network (LSTM) to predict future images based on a sequence of images encoded during video data processing [ 97 ].
• In their research, Salahzadeh et al. [ 98 ] presented a novel mechatronics platform for static and real-time posture analysis, combining 3 complex components. The components included a mechanical structure with cameras, a software module for data collection and semi-automatic image analysis, and a network to provide the raw data to the DL server. The authors concluded that their device, in addition to being inexpensive and easy to use, is a method that allows postural assessment with great stability and in a non-invasive way, proving to be a useful tool in the rehabilitation of patients.
• Studies in graphical search engines and content-based image retrieval (CBIR) systems have also been successfully developed recently [ 11 , 82 , 99 , 100 ], with processing times that might be compatible with real-time applications. Most importantly, the corresponding results of these studies appeared to show adequate image retrieval capabilities, displaying an undisputed similarity between input and output, both on a semantic basis and a graphical basis [ 82 ]. In a review by Latif et al. [ 101 ], the authors concluded that image feature representation, as it is performed, is impossible to be represented by using a unique feature representation. Instead, it should be achieved by a combination of said low-level features, considering they represent the image in the form of patches and, as such, the performance is increased.
• In their publication, Rani et al. [ 102 ] reviewed the current literature found on this topic from the period from 1995 to 2021. The authors found that researchers in microbiology have employed ML techniques for the image recognition of four types of micro-organisms: bacteria, algae, protozoa, and fungi. In their research work, Kasinathan and Uyyala [ 17 ] apply computer vision and knowledge-based approaches to improve insect detection and classification in dense image scenarios. In this work, image processing techniques were applied to extract features, and classification models were built using ML algorithms. The proposed approach used different feature descriptors, such as texture, color, shape, histograms of oriented gradients (HOG) and global image descriptors (GIST). ML was used to analyze multivariety insect data to obtain the efficient utilization of resources and improved classification accuracy for field crop insects with a similar appearance.

As the most popular research area for image processing, research studies using DL in the medical field exist in a wide variety of subjects. Automatic classifiers for imaging purposes can be used in many different medical subjects, often with very good results. However, the variety of devices, locations, and sampling techniques used can often lead to undesired or misunderstood results. One clear advantage of these approaches is that some exams and analyses are based on a human inspection, which can be time-consuming, require extensive training for the personnel, and may also be subject to subjectivity and variability in the observers [ 16 , 103 , 104 ]. In 2023, Luis et al. applied explainable artificial intelligence (xAI) as a way to test the application of different classifiers for monkeypox detection and to better understand the results [ 62 ]. With a greater focus on properly interpreting the model results, approaches such as these are increasingly more common. Recently, Melanthota et al. [ 32 ] conducted a review of research regarding DL-based image processing in optical microscopy. DL techniques can be particularly useful in this topic since manual image analysis of tissue samples tends to be a very tedious and time-consuming process due to the complex nature of the biological entities, while the results can also be highly subjective. The authors concluded that DL models perform well in improving image resolution in smartphone-based microscopy, being an asset in the development and evolution of healthcare solutions in remote locations. The authors also identified an interesting application of DL to monitor gene expression and protein localization in organisms. Overall, it was noted how CNN-based DL networks have emerged as a model with great potential for medical image processing.

Brain image segmentation is a subject addressed by a vast number of researchers who seek to develop systems for accurate cancer diagnosis able to differentiate cancer cells from healthy ones [ 105 , 106 , 107 , 108 , 109 , 110 , 111 ]. A problem that such approaches can mitigate is that human verification of magnetic resonance imaging to locate tumors can be prone to errors. In a recent study, Devunooru et al. [ 105 ] provided a taxonomy system for the key components needed to develop an innovative brain tumor diagnosis system based on DL models. The taxonomy system, named data image segmentation processing and viewing (DIV), comprised research that had been developed since 2016. The results indicated that the majority of the proposed approaches only applied two factors from the taxonomy system, namely data and image segmentation, ignoring a third important factor, which is "view". The comprehensive framework developed by the authors considers all three factors to overcome the limitations of state-of-the-art solutions. Finally, the authors consider that efforts should be made to increase the efficiency of approaches used in image segmentation problems, as well as in problems processing large quantities of medical images.

In their review, Yedder et al. [ 112 ] focused on studying state-of-the-art medical image reconstruction algorithms focused on DL-based methods. The main focus of his research was the reconstruction of biomedical images as an important source of information for the elaboration of medical diagnoses. The authors’ work focused on the differences observed by applying conventional reconstruction methods in contrast to learning-based methods. They showed particular interest in the success of DL in computer vision and medical imaging problems, as well as its recent rise in popularity, concluding that DL-based methods appeared to adequately address the noise sensitivity and the computational inefficiency of iterative methods. Furthermore, the authors noted that the use of DL methods in medical image reconstruction encompassed an ever-increasing number of modalities, noting a clear trend in the newer art toward unsupervised approaches, primarily instigated by the constraints in realistic or real-world training data.

4.1.2. Research Using Reinforcement Learning

Finally, we will finalize our state-of-the-art review by referencing research that used reinforcement learning approaches, mostly in combination with deep learning methods. RL research has been developed in several topics, including robotics [ 113 , 114 , 115 ], design automation [ 25 ], energy management strategies for hybrid vehicles [ 43 ], parameter estimation in the context of biological systems [ 44 , 116 , 117 ], in facial motion learning [ 48 , 50 , 118 ], and have also been successfully applied in closed-world environments, such as games [ 51 , 54 , 119 , 120 ]. In the topic of image processing, some pertinent studies were found, especially using DRL [ 31 , 47 , 57 , 121 ]. Many novel applications continue to be proposed by researchers. A study conducted in 2022 by Dai et al. [ 122 ] explored effective healthcare strategies for simulated human bodies through the combination of DRL methods with conceptual embedding techniques. In this instance, the DNN architecture was used to recreate the transformation function of the input-output characteristics in a human body, using a dataset containing 990 tongue images of nine body constitution (BC) types. The authors concluded that the proposed framework could be particularly useful when applied to a high-dimensional dynamic system of the human body. Amongst the most relevant research encountered, we highlight the following:

In order to overcome the challenges in computer vision, in terms of data-efficiency or generalizing to new environments, a study from 2020 by Laskin et al. [ 49 ] presented a reinforcement learning module with augmented data leveraging, which could be incorporated in typical RL systems to effortlessly improve their overall performance. The authors remarked that data augmentations could potentially improve data efficiency in RL methods operating from pixels, even without significant changes to the underlying RL algorithm. The proposed approach by Laskin et al. [ 49 ] could help make deep RL be more practical for solving real-world problems. In a different example, Khayyat and Elrefaei Khayyat and Elrefaei [ 47 ] successfully developed a system for retrieving ancient images from Arabic manuscripts through an RL agent. The main benefit of this approach was the reduction of data dimensionality, which leads to increased accuracy in image classification and retrieval tasks. Image visual features, extracted using a pre-trained VGG19 convolutional neural network, are fused with textual features through a concatenation and hash merge layer. The success achieved in this scenario may also suggest that the model can be applied to other types of images.

Amongst the recent advancements in DRL focusing on computing optimization is the work presented by Ren et al. [ 57 ], which proposed a system for image stereo-matching algorithms with rule constraints and parallax estimation. Initially, the edge pixel constraint rules were established, and adjustments were made to the image blocks; then, the image parallax estimation was performed, and a DRL analysis was executed by a CNN in an iterative way. The results showed the proposed algorithm was able to complete convergence quickly, with an accuracy of up to more than 95%. However, the matching targets were not clearly defined, particularly in small objects with curved surfaces, which could limit their practicality. Due to a large number of existing models, in 2022, Le et al. [ 31 ] conducted an extensive review of the state-of-the-art advances using DRL in computer vision research. The main objective was to propose a categorization of DRL methodologies, present the potential advantages and limitations in computer vision, and discuss future research directions. The authors propose to divide DRL methods into seven categories, depending on their applications: (i) landmark localization, (ii) object detection, (iii) object tracking, (iv) registration on both 2D image and 3D image volumetric data, (v) image segmentation, (vi) video analysis, and (vii) other applications. Some of the most promising approaches selected by the authors to create new insights into this research field included inverse DRL, multi-agent DRL, meta DRL, and imitation learning.

5. Discussion and Future Directions

Although the advances and successes of ML are undeniable, particularly in the field of digital image processing, there are still important limitations, both in terms of its operational mode and in terms of its design. One of the most important is the fact that, for the most part, the algorithms developed to date are trained to perform a certain task, being able to solve a particular problem. The generalization capacity of existing ML models is limited, making it difficult to apply them to solve problems other than those for which they were trained. Although it is possible to apply learning transfer techniques with the aim of using existing models in new contexts, the results still fall short of the needs.

As previously noted, another one of the limitations we identified concerns the models’ efficiency. ML, in particular DL techniques, requires a large amount of data and computational resources to train and run the models, which may be infeasible or impractical in some scenarios or applications. This requires techniques that can reduce the cost and time of training and inference, as well as increase the robustness and generalization of the models. Some examples of these techniques are model compression, model pruning, model quantization, and knowledge distillation, among others.

Additionally, it is important to highlight the difficulty in interpreting DL models, given their complexity and opacity, which makes it difficult to understand their internal functioning, as well as the results produced. This requires techniques that can explain the functioning, logic, and reliability of models, as well as the factors that influence their decisions. Some examples of these techniques are the visualization of activations, sensitivity analysis, attribution of importance, and generation of counterfacts, among others.

No less important are the limitations that deserve some reflection related to ethics and responsibility since DL has a major impact on society, business, and people. This requires the use of techniques that can guarantee the privacy, security, transparency, justice, and accountability of models, as well as avoid or mitigate their possible negative effects. Some examples of techniques that can help in the mitigation of such limitations are homomorphic encryption, federated learning, algorithmic auditing, and bias detection.

6. Conclusions

In this review, we analyzed some of the most recent works developed in ML, particularly using DL and RL methods or combinations of these. It is becoming increasingly obvious that image processing systems are applied in the most diverse contexts and have seen increasingly more impressive results as the methods have matured. Some of the observed trends appear to indicate a prevalence of certain techniques in certain research topics, which is not surprising. Amongst these trends, we observed:

• Interest in image-processing systems using DL methods has exponentially increased over the last few years. The most common research disciplines for image processing and AI are medicine, computer science, and engineering.
• Traditional ML methods are still extremely relevant and are frequently used in fields such as computational biology and disease diagnosis and prediction or to assist in specific tasks when coupled with other more complex methods. DL methods have become of particular interest in many image-processing problems, particularly because of their ability to circumvent some of the challenges that more traditional approaches face.
• A lot of attention from researchers seems to focus on improving model performance, reducing computational resources and time, and expanding the application of ML models to solve concrete real-world problems.
• The medical field seems to have developed a particular interest in research using multiple classes and methods of learning algorithms. DL image processing has been useful in analyzing medical exams and other imaging applications. Some areas have also still found success using more traditional ML methods.
• Another area of interest appears to be autonomous driving and driver profiling, possibly powered by the increased access to information available both for the drivers and the vehicles alike. Indeed, modern driving assistance systems have already implemented features such as (a) road lane finding, (b) free driving space finding, (c) traffic sign detection and recognition, (d) traffic light detection and recognition, and (e) road-object detection and tracking. This research field will undoubtedly be responsible for many more studies in the near future.
• Graphical search engines and content-based image retrieval systems also present themselves as an interesting topic of research for image processing, with a diverse body of work and innovative approaches.

We found interesting applications using a mix of DL and RL models. The main advantage of these approaches is having the potential of DL to process and classify the data and use reinforcement methods to capitalize on the historical feedback of the performed actions to fine-tune the learning hyperparameters. This is one area that seems to have become a focus point of research, with an increasing number of studies being developed in an area that is still recent. This attention will undoubtedly lead to many new developments and breakthroughs in the following years, particularly in computer vision problems, as this suite of methods becomes more mature and more widely used.

Acknowledgments

We thank the reviewers for their very helpful comments.

Abbreviations

The following abbreviations are used in this manuscript:

Funding Statement

This manuscript is a result of the research project “DarwinGSE: Darwin Graphical Search Engine”, with code CENTRO-01-0247-FEDER-045256, co-financed by Centro 2020, Portugal 2020 and the European Union through the European Regional Development Fund.

Author Contributions

Conceptualization, S.J., J.V. and J.A.; formal analysis, S.J., J.V. and J.A.; funding acquisition, C.M.; Investigation, S.J. and J.V.; methodology, S.J., J.V. and J.A.; project administration, C.M.; supervision, S.J. and C.M.; validation, S.J., J.V., J.A. and C.M.; writing—original draft, J.V. and J.A.; writing—review and editing, S.J., J.V., J.A. and C.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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Digital Image Processing - Contemporary Research Topics

I need to propose a masters degree topic, and I'd like it to be in digital image processing. What are currently relevant topics in this area?

I suppose that I'm looking for some kind of optimization, either in hardware (like parallel processor architecture for image processing maybe) or software (image processing alogrithms optimization of some sort, maybe even in signal processing domain).

I'm leaning towards a software topic, because I think I can manage mathematics and algorithms better than computer engineering, compared to other people I guess.

Please suggest some contemporary topics and areas you believe are suitable for possible optimization, that could be achieved in a masters program :)

• image-processing
• optimization

• 1 $\begingroup$ See these topics: di.ens.fr/~mallat/papiers/CRM-Mallat-Course1.pdf $\endgroup$ –  Deniz Commented Dec 2, 2013 at 21:50
• $\begingroup$ @Deniz This looks interesting, thanks! Heavy maths though, will need some time to figure out what's going on :) $\endgroup$ –  Vidak Commented Dec 3, 2013 at 13:44
• $\begingroup$ once you get the ideas of Fourier theory and with some linear algebra, you can manage it :) Of course, wavelets and those things have heavy theory but one can use them without delving into much theory. $\endgroup$ –  Deniz Commented Dec 7, 2013 at 13:47

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Digital Image Processing Thesis

Digital Image Processing is a technique to work on the set of pixels/bits in a digital image to produce the image in another useful format and it is shortly denoted as DIP. This manipulation of the image will bring a new dimension to the image. Most importantly, it has several unique characteristics that give tremendous creative topics. This page is about the new innovation Digital Image Processing Thesis ideas with important research areas!!!  

Characteristics of Digital Image Processing

• Minimize the complication in processing image
• Assure image quality through noise-free images
• Broadly integrated with other promising fields
• Provide better experience and visual interpretation
• Enhance the weakened or unclear image content
• Supports various open-source software (paid / free)

DIP approach analyzes the image from different angles to give the various perception of the image. Also, it is used to i mprove the quality of the image and extract the essential features of the image through well-defined procedures. Further, we have mentioned the merits of image processing for your knowledge.

Advantages of Digital Image Processing

• Rapid Image Acquisition, Retrieval, and Storage
• Eye Controlled Viewing (Zooming and Windowing)
• 3D Image Reformatting (Multi-view and Multi-plane)
• Quick Image Distribution without compromising quality
• Hybrid Image Reconstruction (CT, PET, MRI, SPECT, PET/CT, SPECT/CT)  

Introduction to Image Processing 

Generally, image processing is the practice of manipulating images through different techniques to generate an enhanced new image . Further, it is also referred to as signal processing which yields image/ image feature as output after processing input image.

• Input:  Features
• Output:  Understanding
• Examples:  Autonomous Driving and Scene Parsing
• Input:  Image
• Output:  Image
• Examples:  Image Sharpening and De-noising
• Output:  Features
• Examples: Image Segmentation and Object Identification

Due to the flexibility in image processing, this field is widely growing in all the leading research areas and applications . On the whole, it becomes the central research field in information and computer science engineering disciplines. Her, we have listed out a few latest Digital Image Processing Thesis ideas .

Research Topics in Digital Image Processing 

• Remote Ultra-High Resolution Image Formation
• 3D Image Display, Printing, and Scanning
• High-Speed Video / Image Synthesis and Processing
• 3 Dimensional Image Display and Acquisition
• Industrial Ultrasound Image and Signal Processing Applications

Majorly, DIP involves two main operations in all the applications. Firstly, it manipulates the image for better computer vision which includes representation and storage. Secondly, it enhances the image quality for human understanding . Also, it falls under any of the below frequencies.  

How do frequencies show up in an image? 

• High frequencies – High variation in pixel intensities (For example edges)
• Low frequencies – Slow variation in pixel intensities (For instance: continuous surface)  

What is Digital Image Processing (DIP)?

Digital Image Processing (DIP) is mathematical operations enabled software which mainly designed to process the computer stored digital images . Through this technique, we can manipulate the images in all aspects to get the fine-tuned information of the image . We guide research scholars to choose interesting digital image processing thesis topics .  For instance: MATLAB, Adobe Photoshop, etc. For your reference, here, we have given the high demanding research areas for the current DIP PhD / MS research study.

Types of an image

• Black and White Image  – The black and white image is exactly referred to as grayscale image which a pixel value may be either absolute white or absolute black
• 8-bit Color Format  – It is also a grayscale image with a different range of values. Here, each pixel represents 8bit color which can display a maximum of 256 different shades of colors. (1 – white, 127 – gray, 255 – back)
• Binary Image  – The binary image is also referred to as Monochrome which contains only a 2-pixel intensity value i.e., 1and 0. (1 – white and 0 – black)
• 16-bit Color Format – It is a High Color Format with 65,536 colors variation. Compare to the grayscale, it has a different format for color distribution

What are the steps in digital image processing?

• Color Space Image Processing
• Image Acquisition and Compression
• Object Recognition and Classification
• Image Enhancement and Restoration
• Wavelets and Multi-resolution Processing
• Morphological Processing and Segmentation
• Knowledge Representation and Description

Further, it is also helpful to enhance the images, view the invisible data, detect the specific object, reconstruct the damaged image, extract the special features , etc. By the by, it is also called digital signal processing which is a covert signal from the image sensor to digital images. Also, we have given the different types of images that we use for processing the image.  

Research Areas in Digital Image Processing 

Recently, our resource team has handpicked the unique research areas for digital image processing thesis. The below areas are very significant to begin your research career. These areas surely shape your knowledge to create a strong foundation in your profession.

• Industrial Applications
• Human-Computer / Machine Interfaces
• Artistic Impact on Image
• Video and Image Processing Architecture
• Medical Image Visualization and Inspection
• Image Restoration and Quality Improvement
• Insight of the image content (Computer Vision)
• Fast Video or Image Labeling, Distributing and Retrieval
• Creation and Synthesis of images (Computer Graphics)

In addition to research and development services, we also give our support in Digital Image Processing Thesis writing. We have a team of native writers to help you in preparing a flawless master thesis. For the best thesis writing service , we follow certain policies in writing your thesis. For your information, we have given the list of criteria that we include in developing the best thesis to publish your work in latest image processing journals list .  

Thesis Writing Format 

• Give a brief overview of the research proposal
• Elaborate the handpicked research question/problem
• Analyze the theoretical context
• Review the related topics from recent history
• Find whether someone makes a debate on gaps
• Identify the research gaps in the selected area
• Survey on problem-solving methods in related works
• Ethics Statement
• Design the system architecture
• Mention what type of data is used as input
• Give the summary flow of research
• Analyze the required costs for execution
• Access and Select the optimal ones
• Acquire the proposed algorithms and methods
• Human Subjects Assessment
• Give an explanation on primary findings
• Analyze and reveal the weakness
• Notify the approaches to be followed
• Give the proofs or evidence for the research need
• Give information about the research scope
• Make a note on relations and categories
• Define the limitation statement
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