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Image processing articles from across Nature Portfolio

Image processing is manipulation of an image that has been digitised and uploaded into a computer. Software programs modify the image to make it more useful, and can for example be used to enable image recognition.

Latest Research and Reviews

image processing research paper topics

Remote sensing image dehazing using generative adversarial network with texture and color space enhancement

  • Chunming Wu

image processing research paper topics

A novel approach to craniofacial analysis using automated 3D landmarking of the skull

  • Franziska Wilke
  • Harold Matthews
  • Susan Walsh

image processing research paper topics

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning

  • Kassandra Walluks
  • Jan-Philipp Praetorius
  • Marc Thilo Figge

image processing research paper topics

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

  • Mohammed Falih Hassan
  • Ahmed Naser Al-Zurfi
  • Khandakar Ahmed

image processing research paper topics

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT

  • Zhongyi Zhang
  • Xiangchun Liu

image processing research paper topics

Automated thorax disease diagnosis using multi-branch residual attention network

  • Dongfang Li


News and Comment

image processing research paper topics

DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible

  • Iván Hidalgo-Cenalmor
  • Joanna W. Pylvänäinen
  • Estibaliz Gómez-de-Mariscal

Big data for everyone

  • Henrietta Howells

image processing research paper topics

Creating a universal cell segmentation algorithm

Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.

Where imaging and metrics meet

When it comes to bioimaging and image analysis, details matter. Papers in this issue offer guidance for improved robustness and reproducibility.

image processing research paper topics

EfficientBioAI: making bioimaging AI models efficient in energy and latency

  • Jianxu Chen

image processing research paper topics

JDLL: a library to run deep learning models on Java bioimage informatics platforms

  • Carlos García López de Haro
  • Stéphane Dallongeville
  • Jean-Christophe Olivo-Marin

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image processing research paper topics

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.

Top 10 Digital Image Processing Project Topics Guidance

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.  

Research Digital Image Processing Project Topics

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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Specialty grand challenge article, grand challenges in image processing.

  • Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des signaux et Systèmes, Gif-sur-Yvette, France


The field of image processing has been the subject of intensive research and development activities for several decades. This broad area encompasses topics such as image/video processing, image/video analysis, image/video communications, image/video sensing, modeling and representation, computational imaging, electronic imaging, information forensics and security, 3D imaging, medical imaging, and machine learning applied to these respective topics. Hereafter, we will consider both image and video content (i.e. sequence of images), and more generally all forms of visual information.

Rapid technological advances, especially in terms of computing power and network transmission bandwidth, have resulted in many remarkable and successful applications. Nowadays, images are ubiquitous in our daily life. Entertainment is one class of applications that has greatly benefited, including digital TV (e.g., broadcast, cable, and satellite TV), Internet video streaming, digital cinema, and video games. Beyond entertainment, imaging technologies are central in many other applications, including digital photography, video conferencing, video monitoring and surveillance, satellite imaging, but also in more distant domains such as healthcare and medicine, distance learning, digital archiving, cultural heritage or the automotive industry.

In this paper, we highlight a few research grand challenges for future imaging and video systems, in order to achieve breakthroughs to meet the growing expectations of end users. Given the vastness of the field, this list is by no means exhaustive.

A Brief Historical Perspective

We first briefly discuss a few key milestones in the field of image processing. Key inventions in the development of photography and motion pictures can be traced to the 19th century. The earliest surviving photograph of a real-world scene was made by Nicéphore Niépce in 1827 ( Hirsch, 1999 ). The Lumière brothers made the first cinematographic film in 1895, with a public screening the same year ( Lumiere, 1996 ). After decades of remarkable developments, the second half of the 20th century saw the emergence of new technologies launching the digital revolution. While the first prototype digital camera using a Charge-Coupled Device (CCD) was demonstrated in 1975, the first commercial consumer digital cameras started appearing in the early 1990s. These digital cameras quickly surpassed cameras using films and the digital revolution in the field of imaging was underway. As a key consequence, the digital process enabled computational imaging, in other words the use of sophisticated processing algorithms in order to produce high quality images.

In 1992, the Joint Photographic Experts Group (JPEG) released the JPEG standard for still image coding ( Wallace, 1992 ). In parallel, in 1993, the Moving Picture Experts Group (MPEG) published its first standard for coding of moving pictures and associated audio, MPEG-1 ( Le Gall, 1991 ), and a few years later MPEG-2 ( Haskell et al., 1996 ). By guaranteeing interoperability, these standards have been essential in many successful applications and services, for both the consumer and business markets. In particular, it is remarkable that, almost 30 years later, JPEG remains the dominant format for still images and photographs.

In the late 2000s and early 2010s, we could observe a paradigm shift with the appearance of smartphones integrating a camera. Thanks to advances in computational photography, these new smartphones soon became capable of rivaling the quality of consumer digital cameras at the time. Moreover, these smartphones were also capable of acquiring video sequences. Almost concurrently, another key evolution was the development of high bandwidth networks. In particular, the launch of 4G wireless services circa 2010 enabled users to quickly and efficiently exchange multimedia content. From this point, most of us are carrying a camera, anywhere and anytime, allowing to capture images and videos at will and to seamlessly exchange them with our contacts.

As a direct consequence of the above developments, we are currently observing a boom in the usage of multimedia content. It is estimated that today 3.2 billion images are shared each day on social media platforms, and 300 h of video are uploaded every minute on YouTube 1 . In a 2019 report, Cisco estimated that video content represented 75% of all Internet traffic in 2017, and this share is forecasted to grow to 82% in 2022 ( Cisco, 2019 ). While Internet video streaming and Over-The-Top (OTT) media services account for a significant bulk of this traffic, other applications are also expected to see significant increases, including video surveillance and Virtual Reality (VR)/Augmented Reality (AR).

Hyper-Realistic and Immersive Imaging

A major direction and key driver to research and development activities over the years has been the objective to deliver an ever-improving image quality and user experience.

For instance, in the realm of video, we have observed constantly increasing spatial and temporal resolutions, with the emergence nowadays of Ultra High Definition (UHD). Another aim has been to provide a sense of the depth in the scene. For this purpose, various 3D video representations have been explored, including stereoscopic 3D and multi-view ( Dufaux et al., 2013 ).

In this context, the ultimate goal is to be able to faithfully represent the physical world and to deliver an immersive and perceptually hyperrealist experience. For this purpose, we discuss hereafter some emerging innovations. These developments are also very relevant in VR and AR applications ( Slater, 2014 ). Finally, while this paper is only focusing on the visual information processing aspects, it is obvious that emerging display technologies ( Masia et al., 2013 ) and audio also plays key roles in many application scenarios.

Light Fields, Point Clouds, Volumetric Imaging

In order to wholly represent a scene, the light information coming from all the directions has to be represented. For this purpose, the 7D plenoptic function is a key concept ( Adelson and Bergen, 1991 ), although it is unmanageable in practice.

By introducing additional constraints, the light field representation collects radiance from rays in all directions. Therefore, it contains a much richer information, when compared to traditional 2D imaging that captures a 2D projection of the light in the scene integrating the angular domain. For instance, this allows post-capture processing such as refocusing and changing the viewpoint. However, it also entails several technical challenges, in terms of acquisition and calibration, as well as computational image processing steps including depth estimation, super-resolution, compression and image synthesis ( Ihrke et al., 2016 ; Wu et al., 2017 ). The resolution trade-off between spatial and angular resolutions is a fundamental issue. With a significant fraction of the earlier work focusing on static light fields, it is also expected that dynamic light field videos will stimulate more interest in the future. In particular, dense multi-camera arrays are becoming more tractable. Finally, the development of efficient light field compression and streaming techniques is a key enabler in many applications ( Conti et al., 2020 ).

Another promising direction is to consider a point cloud representation. A point cloud is a set of points in the 3D space represented by their spatial coordinates and additional attributes, including color pixel values, normals, or reflectance. They are often very large, easily ranging in the millions of points, and are typically sparse. One major distinguishing feature of point clouds is that, unlike images, they do not have a regular structure, calling for new algorithms. To remove the noise often present in acquired data, while preserving the intrinsic characteristics, effective 3D point cloud filtering approaches are needed ( Han et al., 2017 ). It is also important to develop efficient techniques for Point Cloud Compression (PCC). For this purpose, MPEG is developing two standards: Geometry-based PCC (G-PCC) and Video-based PCC (V-PCC) ( Graziosi et al., 2020 ). G-PCC considers the point cloud in its native form and compress it using 3D data structures such as octrees. Conversely, V-PCC projects the point cloud onto 2D planes and then applies existing video coding schemes. More recently, deep learning-based approaches for PCC have been shown to be effective ( Guarda et al., 2020 ). Another challenge is to develop generic and robust solutions able to handle potentially widely varying characteristics of point clouds, e.g. in terms of size and non-uniform density. Efficient solutions for dynamic point clouds are also needed. Finally, while many techniques focus on the geometric information or the attributes independently, it is paramount to process them jointly.

High Dynamic Range and Wide Color Gamut

The human visual system is able to perceive, using various adaptation mechanisms, a broad range of luminous intensities, from very bright to very dark, as experienced every day in the real world. Nonetheless, current imaging technologies are still limited in terms of capturing or rendering such a wide range of conditions. High Dynamic Range (HDR) imaging aims at addressing this issue. Wide Color Gamut (WCG) is also often associated with HDR in order to provide a wider colorimetry.

HDR has reached some levels of maturity in the context of photography. However, extending HDR to video sequences raises scientific challenges in order to provide high quality and cost-effective solutions, impacting the whole imaging processing pipeline, including content acquisition, tone reproduction, color management, coding, and display ( Dufaux et al., 2016 ; Chalmers and Debattista, 2017 ). Backward compatibility with legacy content and traditional systems is another issue. Despite recent progress, the potential of HDR has not been fully exploited yet.

Coding and Transmission

Three decades of standardization activities have continuously improved the hybrid video coding scheme based on the principles of transform coding and predictive coding. The Versatile Video Coding (VVC) standard has been finalized in 2020 ( Bross et al., 2021 ), achieving approximately 50% bit rate reduction for the same subjective quality when compared to its predecessor, High Efficiency Video Coding (HEVC). While substantially outperforming VVC in the short term may be difficult, one encouraging direction is to rely on improved perceptual models to further optimize compression in terms of visual quality. Another direction, which has already shown promising results, is to apply deep learning-based approaches ( Ding et al., 2021 ). Here, one key issue is the ability to generalize these deep models to a wide diversity of video content. The second key issue is the implementation complexity, both in terms of computation and memory requirements, which is a significant obstacle to a widespread deployment. Besides, the emergence of new video formats targeting immersive communications is also calling for new coding schemes ( Wien et al., 2019 ).

Considering that in many application scenarios, videos are processed by intelligent analytic algorithms rather than viewed by users, another interesting track is the development of video coding for machines ( Duan et al., 2020 ). In this context, the compression is optimized taking into account the performance of video analysis tasks.

The push toward hyper-realistic and immersive visual communications entails most often an increasing raw data rate. Despite improved compression schemes, more transmission bandwidth is needed. Moreover, some emerging applications, such as VR/AR, autonomous driving, and Industry 4.0, bring a strong requirement for low latency transmission, with implications on both the imaging processing pipeline and the transmission channel. In this context, the emergence of 5G wireless networks will positively contribute to the deployment of new multimedia applications, and the development of future wireless communication technologies points toward promising advances ( Da Costa and Yang, 2020 ).

Human Perception and Visual Quality Assessment

It is important to develop effective models of human perception. On the one hand, it can contribute to the development of perceptually inspired algorithms. On the other hand, perceptual quality assessment methods are needed in order to optimize and validate new imaging solutions.

The notion of Quality of Experience (QoE) relates to the degree of delight or annoyance of the user of an application or service ( Le Callet et al., 2012 ). QoE is strongly linked to subjective and objective quality assessment methods. Many years of research have resulted in the successful development of perceptual visual quality metrics based on models of human perception ( Lin and Kuo, 2011 ; Bovik, 2013 ). More recently, deep learning-based approaches have also been successfully applied to this problem ( Bosse et al., 2017 ). While these perceptual quality metrics have achieved good performances, several significant challenges remain. First, when applied to video sequences, most current perceptual metrics are applied on individual images, neglecting temporal modeling. Second, whereas color is a key attribute, there are currently no widely accepted perceptual quality metrics explicitly considering color. Finally, new modalities, such as 360° videos, light fields, point clouds, and HDR, require new approaches.

Another closely related topic is image esthetic assessment ( Deng et al., 2017 ). The esthetic quality of an image is affected by numerous factors, such as lighting, color, contrast, and composition. It is useful in different application scenarios such as image retrieval and ranking, recommendation, and photos enhancement. While earlier attempts have used handcrafted features, most recent techniques to predict esthetic quality are data driven and based on deep learning approaches, leveraging the availability of large annotated datasets for training ( Murray et al., 2012 ). One key challenge is the inherently subjective nature of esthetics assessment, resulting in ambiguity in the ground-truth labels. Another important issue is to explain the behavior of deep esthetic prediction models.

Analysis, Interpretation and Understanding

Another major research direction has been the objective to efficiently analyze, interpret and understand visual data. This goal is challenging, due to the high diversity and complexity of visual data. This has led to many research activities, involving both low-level and high-level analysis, addressing topics such as image classification and segmentation, optical flow, image indexing and retrieval, object detection and tracking, and scene interpretation and understanding. Hereafter, we discuss some trends and challenges.

Keypoints Detection and Local Descriptors

Local imaging matching has been the cornerstone of many analysis tasks. It involves the detection of keypoints, i.e. salient visual points that can be robustly and repeatedly detected, and descriptors, i.e. a compact signature locally describing the visual features at each keypoint. It allows to subsequently compute pairwise matching between the features to reveal local correspondences. In this context, several frameworks have been proposed, including Scale Invariant Feature Transform (SIFT) ( Lowe, 2004 ) and Speeded Up Robust Features (SURF) ( Bay et al., 2008 ), and later binary variants including Binary Robust Independent Elementary Feature (BRIEF) ( Calonder et al., 2010 ), Oriented FAST and Rotated BRIEF (ORB) ( Rublee et al., 2011 ) and Binary Robust Invariant Scalable Keypoints (BRISK) ( Leutenegger et al., 2011 ). Although these approaches exhibit scale and rotation invariance, they are less suited to deal with large 3D distortions such as perspective deformations, out-of-plane rotations, and significant viewpoint changes. Besides, they tend to fail under significantly varying and challenging illumination conditions.

These traditional approaches based on handcrafted features have been successfully applied to problems such as image and video retrieval, object detection, visual Simultaneous Localization And Mapping (SLAM), and visual odometry. Besides, the emergence of new imaging modalities as introduced above can also be beneficial for image analysis tasks, including light fields ( Galdi et al., 2019 ), point clouds ( Guo et al., 2020 ), and HDR ( Rana et al., 2018 ). However, when applied to high-dimensional visual data for semantic analysis and understanding, these approaches based on handcrafted features have been supplanted in recent years by approaches based on deep learning.

Deep Learning-Based Methods

Data-driven deep learning-based approaches ( LeCun et al., 2015 ), and in particular the Convolutional Neural Network (CNN) architecture, represent nowadays the state-of-the-art in terms of performances for complex pattern recognition tasks in scene analysis and understanding. By combining multiple processing layers, deep models are able to learn data representations with different levels of abstraction.

Supervised learning is the most common form of deep learning. It requires a large and fully labeled training dataset, a typically time-consuming and expensive process needed whenever tackling a new application scenario. Moreover, in some specialized domains, e.g. medical data, it can be very difficult to obtain annotations. To alleviate this major burden, methods such as transfer learning and weakly supervised learning have been proposed.

In another direction, deep models have been shown to be vulnerable to adversarial attacks ( Akhtar and Mian, 2018 ). Those attacks consist in introducing subtle perturbations to the input, such that the model predicts an incorrect output. For instance, in the case of images, imperceptible pixel differences are able to fool deep learning models. Such adversarial attacks are definitively an important obstacle to the successful deployment of deep learning, especially in applications where safety and security are critical. While some early solutions have been proposed, a significant challenge is to develop effective defense mechanisms against those attacks.

Finally, another challenge is to enable low complexity and efficient implementations. This is especially important for mobile or embedded applications. For this purpose, further interactions between signal processing and machine learning can potentially bring additional benefits. For instance, one direction is to compress deep neural networks in order to enable their more efficient handling. Moreover, by combining traditional processing techniques with deep learning models, it is possible to develop low complexity solutions while preserving high performance.

Explainability in Deep Learning

While data-driven deep learning models often achieve impressive performances on many visual analysis tasks, their black-box nature often makes it inherently very difficult to understand how they reach a predicted output and how it relates to particular characteristics of the input data. However, this is a major impediment in many decision-critical application scenarios. Moreover, it is important not only to have confidence in the proposed solution, but also to gain further insights from it. Based on these considerations, some deep learning systems aim at promoting explainability ( Adadi and Berrada, 2018 ; Xie et al., 2020 ). This can be achieved by exhibiting traits related to confidence, trust, safety, and ethics.

However, explainable deep learning is still in its early phase. More developments are needed, in particular to develop a systematic theory of model explanation. Important aspects include the need to understand and quantify risk, to comprehend how the model makes predictions for transparency and trustworthiness, and to quantify the uncertainty in the model prediction. This challenge is key in order to deploy and use deep learning-based solutions in an accountable way, for instance in application domains such as healthcare or autonomous driving.

Self-Supervised Learning

Self-supervised learning refers to methods that learn general visual features from large-scale unlabeled data, without the need for manual annotations. Self-supervised learning is therefore very appealing, as it allows exploiting the vast amount of unlabeled images and videos available. Moreover, it is widely believed that it is closer to how humans actually learn. One common approach is to use the data to provide the supervision, leveraging its structure. More generally, a pretext task can be defined, e.g. image inpainting, colorizing grayscale images, predicting future frames in videos, by withholding some parts of the data and by training the neural network to predict it ( Jing and Tian, 2020 ). By learning an objective function corresponding to the pretext task, the network is forced to learn relevant visual features in order to solve the problem. Self-supervised learning has also been successfully applied to autonomous vehicles perception. More specifically, the complementarity between analytical and learning methods can be exploited to address various autonomous driving perception tasks, without the prerequisite of an annotated data set ( Chiaroni et al., 2021 ).

While good performances have already been obtained using self-supervised learning, further work is still needed. A few promising directions are outlined hereafter. Combining self-supervised learning with other learning methods is a first interesting path. For instance, semi-supervised learning ( Van Engelen and Hoos, 2020 ) and few-short learning ( Fei-Fei et al., 2006 ) methods have been proposed for scenarios where limited labeled data is available. The performance of these methods can potentially be boosted by incorporating a self-supervised pre-training. The pretext task can also serve to add regularization. Another interesting trend in self-supervised learning is to train neural networks with synthetic data. The challenge here is to bridge the domain gap between the synthetic and real data. Finally, another compelling direction is to exploit data from different modalities. A simple example is to consider both the video and audio signals in a video sequence. In another example in the context of autonomous driving, vehicles are typically equipped with multiple sensors, including cameras, LIght Detection And Ranging (LIDAR), Global Positioning System (GPS), and Inertial Measurement Units (IMU). In such cases, it is easy to acquire large unlabeled multimodal datasets, where the different modalities can be effectively exploited in self-supervised learning methods.

Reproducible Research and Large Public Datasets

The reproducible research initiative is another way to further ensure high-quality research for the benefit of our community ( Vandewalle et al., 2009 ). Reproducibility, referring to the ability by someone else working independently to accurately reproduce the results of an experiment, is a key principle of the scientific method. In the context of image and video processing, it is usually not sufficient to provide a detailed description of the proposed algorithm. Most often, it is essential to also provide access to the code and data. This is even more imperative in the case of deep learning-based models.

In parallel, the availability of large public datasets is also highly desirable in order to support research activities. This is especially critical for new emerging modalities or specific application scenarios, where it is difficult to get access to relevant data. Moreover, with the emergence of deep learning, large datasets, along with labels, are often needed for training, which can be another burden.

Conclusion and Perspectives

The field of image processing is very broad and rich, with many successful applications in both the consumer and business markets. However, many technical challenges remain in order to further push the limits in imaging technologies. Two main trends are on the one hand to always improve the quality and realism of image and video content, and on the other hand to be able to effectively interpret and understand this vast and complex amount of visual data. However, the list is certainly not exhaustive and there are many other interesting problems, e.g. related to computational imaging, information security and forensics, or medical imaging. Key innovations will be found at the crossroad of image processing, optics, psychophysics, communication, computer vision, artificial intelligence, and computer graphics. Multi-disciplinary collaborations are therefore critical moving forward, involving actors from both academia and the industry, in order to drive these breakthroughs.

The “Image Processing” section of Frontier in Signal Processing aims at giving to the research community a forum to exchange, discuss and improve new ideas, with the goal to contribute to the further advancement of the field of image processing and to bring exciting innovations in the foreseeable future.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

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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Keywords: image processing, immersive, image analysis, image understanding, deep learning, video processing

Citation: Dufaux F (2021) Grand Challenges in Image Processing. Front. Sig. Proc. 1:675547. doi: 10.3389/frsip.2021.675547

Received: 03 March 2021; Accepted: 10 March 2021; Published: 12 April 2021.

Reviewed and Edited by:

Copyright © 2021 Dufaux. 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: Frédéric Dufaux, [email protected]

Research Topics

Biomedical Imaging

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

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

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

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

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

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

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

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.

Deep Evolution Figure

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.

Discovered Radiomics Sequencer

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. 

Discovered Radiomics Sequencer

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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image processing research paper topics

Computer Vision and Image Processing

6th International Conference, CVIP 2021, Rupnagar, India, December 3–5, 2021, Revised Selected Papers, Part I

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  • Balasubramanian Raman 0 ,
  • Subrahmanyam Murala 1 ,
  • Ananda Chowdhury 2 ,
  • Abhinav Dhall 3 ,
  • Puneet Goyal 4

Indian Institute of Technology Roorkee, Roorkee, India

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1567)

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Table of contents (50 papers)

Front matter, classification of brain tumor mr images using transfer learning and machine learning models.

  • LillyMaheepa Pavuluri, Malaya Kumar Nath

Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning

  • Bisakh Mondal, Shuvayan Ghosh Dastidar, Nibaran Das

Computer Aided Diagnosis of Autism Spectrum Disorder Based on Thermal Imaging

  • Kavya Ganesh, Snekhalatha Umapathy, Palani Thanaraj Krishnan

Efficient High-Resolution Image-to-Image Translation Using Multi-Scale Gradient U-Net

  • Kumarapu Laxman, Shiv Ram Dubey, Baddam Kalyan, Satya Raj Vineel Kojjarapu

Generic Multispectral Image Demosaicking Algorithm and New Performance Evaluation Metric

  • Vishwas Rathi, Puneet Goyal

A Platform for Large Scale Auto Annotation of Scanned Documents Featuring Real-Time Model Building and Model Pooling

  • Komuravelli Prashanth, Boyalakuntla Kowndinya, Chilaka Vijay, Dande Teja, Vidya Rodge, Ramya Velaga et al.

AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images

  • Anirudh Ambati, Shiv Ram Dubey

Application of Deep Learning Techniques for Prostate Cancer Grading Using Histopathological Images

  • Mahesh Gour, Sweta Jain, Uma Shankar

Dyadic Interaction Recognition Using Dynamic Representation and Convolutional Neural Network

  • R. Newlin Shebiah, S. Arivazhagan

Segmentation of Unstructured Scanned Devanagari Newspaper Documents

  • Rupinder Pal Kaur, Munish Kumar, M. K. Jindal

Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection

  • Anu Singha, Priya Saha, Mrinal Kanti Bhowmik

Signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures

  • Manish Kumar Srivastava, Dileep Reddy, Bhargav Kurma, Kalidas Yeturu

Leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm

  • Shrikrishna Kolhar, Jayant Jagtap

Fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map

  • Jagannath Sethi, Jaydeb Bhaumik, Ananda S. Chowdhury

Visual Localization Using Capsule Networks

  • Omkar Patil

Detection of Cataract from Fundus Images Using Deep Transfer Learning

  • Subin Sahayam, J. Silambarasan, Umarani Jayaraman

Brain Tumour Segmentation Using Convolution Neural Network

  • Karuna Bhalerao, Shital Patil, Surendra Bhosale

Signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature

  • Anamika Jain, Satish Kumar Singh, Krishna Pratap Singh

A Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth Images

  • Shome S. Das

Other volumes

  • artificial intelligence
  • color image processing
  • color images
  • computer systems
  • computer vision
  • image analysis
  • image matching
  • image processing
  • image quality
  • image reconstruction
  • image segmentation
  • imaging systems
  • machine learning
  • neural networks
  • pattern recognition
  • signal processing

About this book

Editors and affiliations.

Balasubramanian Raman

Subrahmanyam Murala, Abhinav Dhall, Puneet Goyal

Ananda Chowdhury

Bibliographic Information

Book Title : Computer Vision and Image Processing

Book Subtitle : 6th International Conference, CVIP 2021, Rupnagar, India, December 3–5, 2021, Revised Selected Papers, Part I

Editors : Balasubramanian Raman, Subrahmanyam Murala, Ananda Chowdhury, Abhinav Dhall, Puneet Goyal

Series Title : Communications in Computer and Information Science


Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Softcover ISBN : 978-3-031-11345-1 Published: 24 July 2022

eBook ISBN : 978-3-031-11346-8 Published: 23 July 2022

Series ISSN : 1865-0929

Series E-ISSN : 1865-0937

Edition Number : 1

Number of Pages : XXXI, 586

Number of Illustrations : 59 b/w illustrations, 218 illustrations in colour

Topics : Computer Imaging, Vision, Pattern Recognition and Graphics , Artificial Intelligence , Computer Systems Organization and Communication Networks , Computers and Education , Computer Applications

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image processing research paper topics

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Electrical Engineering and Systems Science > Image and Video Processing

Title: sparse focus network for multi-source remote sensing data classification.

Abstract: Multi-source remote sensing data classification has emerged as a prominent research topic with the advancement of various sensors. Existing multi-source data classification methods are susceptible to irrelevant information interference during multi-source feature extraction and fusion. To solve this issue, we propose a sparse focus network for multi-source data classification. Sparse attention is employed in Transformer block for HSI and SAR/LiDAR feature extraction, thereby the most useful self-attention values are maintained for better feature aggregation. Furthermore, cross-attention is used to enhance multi-source feature interactions, and further improves the efficiency of cross-modal feature fusion. Experimental results on the Berlin and Houston2018 datasets highlight the effectiveness of SF-Net, outperforming existing state-of-the-art methods.

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    This two-volume set (CCIS 1567-1568) constitutes the refereed proceedings of the 6h International Conference on Computer Vision and Image Processing, CVIP 2021, held in Rupnagar, India, in December 2021. The 70 full papers and 20 short papers were carefully reviewed and selected from the 260 submissions.

  20. Image Processing

    Image processing applied to medical research has made many clinical diagnosis protocols and treatment plans more efficient and accurate. For example, ... Image processing and computer vision are topics covered by many excellent books. Most known (and yet unknown) algorithms can be devised from the hints offered by their authors by conveniently ...

  21. Image Processing Based Project Topics With Abstracts and Base Papers

    Explore the latest M.Tech project topics in Image Processing for 2024, featuring trending IEEE base papers. Elevate your research with cutting-edge projects covering diverse aspects of visual intelligence, from computer vision to deep learning applications. Discover innovative titles, abstracts, and base papers to stay ahead in the dynamic field of Image Processing.

  22. Digital Image Processing

    In this paper we give a tutorial overview of the field of digital image processing. Following a brief discussion of some basic concepts in this area, image processing algorithms are presented with emphasis on fundamental techniques which are broadly applicable to a number of applications. In addition to several real-world examples of such techniques, we also discuss the applicability of ...

  23. Sparse Focus Network for Multi-Source Remote Sensing Data Classification

    Multi-source remote sensing data classification has emerged as a prominent research topic with the advancement of various sensors. Existing multi-source data classification methods are susceptible to irrelevant information interference during multi-source feature extraction and fusion. To solve this issue, we propose a sparse focus network for multi-source data classification. Sparse attention ...