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Developing Digital Photomicroscopy

(1) The need for efficient ways of recording and presenting multicolour immunohistochemistry images in a pioneering laboratory developing new techniques motivated a move away from photography to electronic and ultimately digital photomicroscopy. (2) Initially broadcast quality analogue cameras were used in the absence of practical digital cameras. This allowed the development of digital image processing, storage and presentation. (3) As early adopters of digital cameras, their advantages and limitations were recognised in implementation. (4) The adoption of immunofluorescence for multiprobe detection prompted further developments, particularly a critical approach to probe colocalization. (5) Subsequently, whole-slide scanning was implemented, greatly enhancing histology for diagnosis, research and teaching.

Parallel Algorithm of Digital Image Processing Based on GPU

Quantitative identification cracks of heritage rock based on digital image technology.

Abstract Digital image processing technologies are used to extract and evaluate the cracks of heritage rock in this paper. Firstly, the image needs to go through a series of image preprocessing operations such as graying, enhancement, filtering and binaryzation to filter out a large part of the noise. Then, in order to achieve the requirements of accurately extracting the crack area, the image is again divided into the crack area and morphological filtering. After evaluation, the obtained fracture area can provide data support for the restoration and protection of heritage rock. In this paper, the cracks of heritage rock are extracted in three different locations.The results show that the three groups of rock fractures have different effects on the rocks, but they all need to be repaired to maintain the appearance of the heritage rock.

Determination of Optical Rotation Based on Liquid Crystal Polymer Vortex Retarder and Digital Image Processing

Discussion on curriculum reform of digital image processing under the certification of engineering education, influence and application of digital image processing technology on oil painting creation in the era of big data, geometric correction analysis of highly distortion of near equatorial satellite images using remote sensing and digital image processing techniques, color enhancement of low illumination garden landscape images.

The unfavorable shooting environment severely hinders the acquisition of actual landscape information in garden landscape design. Low quality, low illumination garden landscape images (GLIs) can be enhanced through advanced digital image processing. However, the current color enhancement models have poor applicability. When the environment changes, these models are easy to lose image details, and perform with a low robustness. Therefore, this paper tries to enhance the color of low illumination GLIs. Specifically, the color restoration of GLIs was realized based on modified dynamic threshold. After color correction, the low illumination GLI were restored and enhanced by a self-designed convolutional neural network (CNN). In this way, the authors achieved ideal effects of color restoration and clarity enhancement, while solving the difficulty of manual feature design in landscape design renderings. Finally, experiments were carried out to verify the feasibility and effectiveness of the proposed image color enhancement approach.

Discovery of EDA-Complex Photocatalyzed Reactions Using Multidimensional Image Processing: Iminophosphorane Synthesis as a Case Study

Abstract Herein, we report a multidimensional screening strategy for the discovery of EDA-complex photocatalyzed reactions using only photographic devices (webcam, cellphone) and TLC analysis. An algorithm was designed to identify automatically EDA-complex reactive mixtures in solution from digital image processing in a 96-wells microplate and by TLC-analysis. The code highlights the region of absorption of the mixture in the visible spectrum, and the quantity of the color change through grayscale values. Furthermore, the code identifies automatically the blurs on the TLC plate and classifies the mixture as colorimetric reactions, non-reactive or potentially reactive EDA mixtures. This strategy allowed us to discover and then optimize a new EDA-mediated approach for obtaining iminophosphoranes in up to 90% yield.

Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques

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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.

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.

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Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity

Identifying active compounds for a target is time- and resource-intensive. Here, the authors show that deep learning models trained on Cell Painting and single-point activity data, can reliably predict compound activity across diverse targets while maintaining high hit rates and scaffold diversity.

• Johan Fredin Haslum
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MRI radiomics in head and neck cancer from reproducibility to combined approaches

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A comparative analysis of pairwise image stitching techniques for microscopy images

• Fatemeh Sadat Mohammadi
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Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

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Joint transformer architecture in brain 3D MRI classification: its application in Alzheimer’s disease classification

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Automated quantification of avian influenza virus antigen in different organs

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News and Comment

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.

EfficientBioAI: making bioimaging AI models efficient in energy and latency

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

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Moving towards a generalized denoising network for microscopy

The visualization and analysis of biological events using fluorescence microscopy is limited by the noise inherent in the images obtained. Now, a self-supervised spatial redundancy denoising transformer is proposed to address this challenge.

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Imaging across scales

New twists on established methods and multimodal imaging are poised to bridge gaps between cellular and organismal imaging.

• Rita Strack

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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

Introduction

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]

Digital Image Processing and IoT in Smart Health Care -A review

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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 Tutorial

Introduction to digital image processing.

• Digital Image Processing Basics
• What is a Pixel?

Image Conversion

• MATLAB | RGB image representation
• How to Convert RGB Image to Binary Image Using MATLAB?
• YIQ Color Model in Computer Graphics
• How to Convert YIQ Image to RGB Image Using MATLAB?
• How to Convert RGB Image to YIQ Image using MATLAB?
• MATLAB | RGB image to grayscale image conversion
• MATLAB | Change the color of background pixels by OTSU Thresholding
• How to Converting RGB Image to HSI Image in MATLAB?
• How to Convert HSI Image to RGB Image in MATLAB?
• How to Partially Colored Gray Image in MATLAB?
• HSV Color Model in Computer Graphics
• How to Color Slicing Using HSV Color Space in MATLAB?

Image Filtering Techniques

Spatial Filtering and its Types

Frequency Domain Filters and its Types

• How to Remove Salt and Pepper Noise from Image Using MATLAB?
• How to Decide Window Size for a Moving Average Filter in MATLAB?

Noise Models in Digital Image Processing

• How to Apply Median Filter For RGB Image in MATLAB?
• How to Linear Filtering Without Using Imfilter Function in MATLAB?
• Noise addition using in-built Matlab function
• Adaptive Filtering - Local Noise Filter in MATLAB
• Difference between Low pass filter and High pass filter
• MATLAB - Butterworth Lowpass Filter in Image Processing
• MATLAB - Ideal Lowpass Filter in Image Processing
• MATLAB | Converting a Grayscale Image to Binary Image using Thresholding
• Laplacian of Gaussian Filter in MATLAB
• What is Upsampling in MATLAB?
• Upsampling in Frequency Domain in MATLAB

Convolution Shape (full/same/valid) in MATLAB

Linear Convolution using C and MATLAB

Histogram Equalization

Histogram Equalization in Digital Image Processing

• Histogram Equalization Without Using histeq() Function in MATLAB
• MATLAB | Display histogram of a grayscale Image
• What Color Histogram Equalization in MATLAB?
• Histogram of an Image

Object Identification and Edge Detection

• Functions in MATLAB
• Program to determine the quadrant of the cartesian plane
• How To Identifying Objects Based On Label in MATLAB?
• What is Image shading in MATLAB?

Edge detection using in-built function in MATLAB

Digital Image Processing Algorithms using MATLAB

• MATLAB - Image Edge Detection using Sobel Operator from Scratch
• Image Complement in Matlab
• Image Sharpening Using Laplacian Filter and High Boost Filtering in MATLAB

PhotoShop Effects in MATLAB

• What is Swirl Effect in MATLAB?
• What is Oil Painting in MATLAB?
• Cone Effect in MATLAB
• What is Glassy Effect in MATLAB?
• What is Tiling Effect in MATLAB?

Image Geometry, Optical Illusion and Image Transformation

Matlab program to rotate an image 180 degrees clockwise without using function

Image Resizing in Matlab

• Nearest-Neighbor Interpolation Algorithm in MATLAB
• Black and White Optical illusion in MATLAB

MATLAB | Complement colors in a Binary image

• Discrete Cosine Transform (Algorithm and Program)
• 2-D Inverse Cosine Transform in MATLAB
• MATLAB - Intensity Transformation Operations on Images
• Fast Fourier Transformation for polynomial multiplication
• Gray Scale to Pseudo Color Transformation in MATLAB
• Piece-wise Linear Transformation
• Balance Contrast Enhancement Technique in MATLAB

Morphologiocal Image Processing, Compression and Files

• Boundary Extraction of image using MATLAB
• MATLAB: Connected Component Labeling without Using bwlabel or bwconncomp Functions
• Morphological operations in MATLAB
• Matlab | Erosion of an Image
• Auto Cropping- Based on Labeling the Connected Components using MATLAB
• Run Length Encoding & Decoding in MATLAB
• Lossless Predictive Coding in MATLAB
• Extract bit planes from an Image in Matlab
• How to Read Text File Backwards Using MATLAB?
• MATLAB - Read Words in a File in Reverse Order
• How to Read Image File or Complex Image File in MATLAB?

Image Coding, Comparison and Texture Features

• Digital Watermarking and its Types
• How To Hide Message or Image Inside An Image In MATLAB?
• How to Match a Template in MATLAB?
• Grey Level Co-occurrence Matrix in MATLAB
• MATLAB - Texture Measures from GLCM

Difference Between

Difference Between RGB, CMYK, HSV, and YIQ Color Models

• Difference between Dilation and Erosion

In this tutorial, we will learn all about Digital Image Processing or DIP which is a subcategory of signal processing that particularly deals with the manipulation of digital images by using a digital computer. It is based on the principle of the I-P-O cycle, where it will take a digital image as an input and process it to get an image as an output.

This free DIP tutorial is designed from basics to advanced level so anyone, from beginners to professionals can easily get all the details of digital image processing. This tutorial includes all the modules like basics, MATLAB GUI, Image conversion, image filtering techniques, histogram equalization, object identification, edge detection in MATLAB, Image extensions, MATLAB built-in functions, array functions, etc.

Table of Content

What is Digital Image Processing (DIP)?

Working of digital image processing, introduction to dip (digital image processing), morphological image processing, compression and files, image transformation.

Digital Image Processing or DIP is software that takes a digital image as an input to process it to get an image as an output. In other words, DIP deals with the manipulation of digital images by using a digital computer. It is a subcategory of signals and systems where it can be easily understood by beginners if they know the basics of digital electronics.

It also requires the knowledge of basic calculus, probability, and differential equations for mathematical calculation purposes. Also, there is a basic knowledge of programming like MATLAB , C++ , etc.

Digital Image Processing is used as a software for image processing . Photography is the mechanism that processes the image accordingly. Other examples are computer graphics, pixels, etc.

Examples of Digital Image Processing

The most common and highly used example is Adobe photoshop. As we discussed above, other examples are computer graphics, photography, camera, X Ray images, CT Scans, etc.

The working principle of Digital Image Processing is based on I-P-O cycle where takes input, process and then release output. Here, digital image acts as an input and after processing, it will become the desired image we want acts as an output. Lets take an example to elaborate the working of Digital Image Processing.

If we take a snap from a camera or mobile, and send that snap to digital system so that it can remove all the irrelevant part of that image and can just enhance the part we want. Like, a snap contains a view of a village and we want to just focus on 3rd cottage, then digital system will just focuses on that only by zooming it and the quality will not get compromised.

Digital Image Processing (DIP) is a field of study and practice on manipulating and analyzing of digital images using digital Algorithms. It deals with the images which are represented in the digital format, basically array of pixels. In DIP, the images are processed to enhance their quality or to perform various operation such as segmentation, restoration and recognition. It has various Applications in domains such as medicine, remote sensing, surveillance and more.

For Example, DIP Techniques are used to enhance clarity of X-ray images which provides healthcare professionals to detect abnormalities more accurately.

What is Image Processing ?

What is an Image in DIP?

What is Digital Image Processing ?

Fundamental Steps in Digital Image Processing

Components of Digital Image Processing

Applications of Digital Image Processing

Analog Image Processing

History of Photography

Concept of Dimensions in DIP

Image Formation on Camera

Camera Mechanism

What is Pixel?

Concept of Bits Per Pixel

Types of Images

Binary Image

Concept of Sampling

Pixel Resolution

Image conversion is the process of converting images from one format to another format. The image conversion is important in digital image processing as different devices and software supports different image formats. Image conversion can be performed using different tools and services. Overall image conversion is an important and valuable process that allows user to change image format for different platforms, devices, and usage scenarios, ensuring compatibility, efficiency, and optimal visual quality.

Introduction to Digital Images

Color Codes Conversion

RGB image representation

How to convert RGB image to binary image

YIQ Color Model

How to convert YIQ image to RGB image

How to convert RGB image to YIQ image

What is Grayscale image?

What are the advantages and disadvantages of HSV color ?

How to convert RGB image to grayscale image without using rgb2gray function

What is Otsu Thresholding ?

Otsu’s thresholding without graythresh function

What is HSI Color Space ?

Converting RGB Image to HSI Image

Converting HSI Image to RGB Image

How to partially colored gray image in MATLAB

What is the HSV (Hue, Saturation, Value) Color Model?

Color Slicing using HSV color space

Image Filtering Techniques are one of the important tools that are used in the digital image processing which provides different methods to improve, modify or extract information from the images. One of the the primary functions of the image processing is to reduce the unwanted noise that can degrade the image quality, to reduce this ,image filtering techniques are used to smooth out irregularities and improve the clarity of image. Overall image filter are important in the digital image processing to perform various functions such as reducing noise, detecting edges, enhancing details, or manipulating colors.

What are Image Filtering Techniques ?

What is Median filtering ?

Median filtering for salt and pepper noise without using medfilt2 function

What is moving average filter ?

How to decide window size for a moving average filter?

Gaussian Noise

Rayleigh Noise

Erlang (or gamma) Noise

Exponential Noise

Uniform Noise

Impulse Noise 

How to apply Median filter for RGB image in MATLAB

Linear filtering without using imfilter function

Add ‘Salt and pepper’or ‘Speckle’ noise to an image

What is Linear filtering ?

What is Adaptive filtering- Local Noise filter

One Dimensional Low pass filtering

High Pass filtering

Band pass filtering

MATLAB – Converting a Grayscale Image to Binary Image using Thresholding

Gaussian Filtering/ Gaussian Blur

Upsampling in MATLAB

Upsampling in Frequency Domain

Circular Convolution using C and MATLAB

Histogram Equalization is one of the fundamental technique used in image processing to improve contrast and brightness of the image. This method works by adjusting the Pixel density distribution of an image to create more uniform histograms which results in improving the visual quality and clarity. Histogram equalization is used in the various image processing such as medical imaging, satellite imaging, digital photography, and computer vision.

Histogram equalization without using histeq() function.[Global]

Local Histogram equalization in MATLAB

Color Histogram equalization

Identifying objects based on label(bwlabel)

Object Identification and Edge Detection are techniques of digital image processing where we can find out the boundaries or area covered by objects within an image and it can be done by using image segmentation and data extraction. It is useful and implemented in many areas like computer and machine vision, image processing, medical imaging, remote sensing, pattern recognition. It can be implemented by using 4 important steps named as smoothing, enhancement, detection and localization.

MATLAB output function

Cartesian to Polar co-ordinates

Image shading in MATLAB

Sobel edge detection

Edge Detection -Fundamentals

Image Sharpening using second order derivative – (Laplacian)

Edge detection using Local Variance

In MATLAB, users can replicate various photoshop effects by using various image processing techniques. By using these techniques user can make similar photoshop visual effects like Adobe Photoshops. For this process, MATLAB is used as it is flexible and it has extensive toolbox which makes it a powerful platform for implementing a wide range of image effects and enhancements. These includes effects like motion blur, sharpening techniques, color adjustments, denoising algorithms, artistic effects such as oil painting or watercolor, geometric transformations, text overlays, and more.

What is Swirl Effect in MATLAB

Oil Painting in MATLAB

Cone effect in MATLAB

Glassy effect in MATLAB

Tiling effect in MATLAB

These techniques are the integral components of the digital image processing. The image Geometry is used to deal with the spatial arrangements in the digital images, it includes techniques for image adjustment, such as resizing, cropping, and rotating. The Optical illusion are used for visual Phenomena, it contains various techniques to create or replicate optical illusions. The Image Transformation are the broad range of techniques used for altering the appearance or content of digital images. These Techniques has various applications in photography, graphic design, medical imaging, and scientific research.

Geometric Transformation in Image Processing

Image rotation by Matlab without using imrotate

Optical Illusion in Digital Image Processing

Nearest-neighbor interpolation algorithm in MATLAB

Black and white optical illusion

2-D Discrete Cosine Transform

2-D Inverse Cosine Transform

Intensity Transformation Operations on Images

What is Fast Fourier Transform?

Fast Fourier Transform on 2 Dimensional matrix using MATLAB

Gray Level Transformation

Gray Scale to Pseudo Color Transformation

What is Image Enhancement?

Linear Contrast Enhancement

Balance Contrast Enhancement Technique

These Processing techniques are the important components of digital image processing. The Morphological Image Processing involves the analysis and manipulation of image shape and structure using mathematical techniques which includes function such as dilation, erosion, opening, and closing, that are used to enhance, segment, or extract features from images based on their geometric properties.

The Compression is the techniques used to reduce the size of the digital image file while preserving its quality. This is done by eliminating redundant or irrelevant information from images. The File handling technique is the process of managing, manipulating and storing the image files in the different formats. These techniques has wide range of applications in science, engineering, medicine, and multimedia.

What is morphological image processing?

What are data compression techniques?

Boundary Extraction

MATLAB: Connected Component labeling without using bwlabel or bwconncomp functions

Morphological Operations in MATLAB

MATLAB Erosion of an Image

Auto Cropping- Based on labeling the connected components

Run Length Encoding & Decoding in MATLAB

Lossless Predictive Coding

Bit Plane Compression

How to read text file backwards?

Read words in a file in reverse order

How to read image file or complex image file in MATLAB

Image Transformation is the process of changing or alerting the appearance or characteristics of digital images through various mathematical operations. These operations involves different techniques to modify the spatial properties of images such as size, orientation, and position to get the desired visual effect or to extract the specific information. Image Transformation is widely used in the DIP for performing operation such as geometric correction, image registration, and feature extraction.

What is Image Coding ?

What is image comparison?

What is a texture in image processing?

Image Coding ,Comparison and Text features are the fundamental aspects in the DIP. Image coding is the techniques for efficiently representing and compressing the digital images. It contains different compression algorithms such as JPEG , PNG , and GIF . Comparison techniques are used for assessing similarities or differences between images. It includes Methods like computing the similarity metrics, such as correlation coefficients or structural similarity indices, to find the degree of resemblance between images.

The Texture feature is the Spatial arrangement of pixel intensities in an image that provides the information about surface properties such as smoothness, roughness, or granularity. These Techniques plays important role in advancing the field of digital image processing.

Digital Image Watermarking

Hide the message or image inside an image

How do I match a template in Matlab?

Grey Level Co-occurrence Matrix (GLCM) in MATLAB

Texture Measures from GLCM – MATLAB CODE

This part of the tutorial contains important differences or comparison based topics based on digital image processing where you can see differences of types of images, color models, types of noise etc. All the differences articles will give you an structured tabular format differences for comparison with clarity.

Analog Image Processing vs. Digital Image Processing

Difference between dilation and Erosion

Differentiate between grayscale and RGB images

Monochrome vs. Grayscale

Difference between binary and grayscale images?

Differences between RGB and CMYK color schemes

Portable Cameras vs. Digital Cameras

Difference between 8-bit and 16 bit color format

Difference between salt noise and pepper noise

With a clear focus on practical techniques and real-world applications, this digital image processing tutorial equips learners with the essential skills to navigate the complexities of digital image processing seamlessly on the areas MATLAB GUI, Image conversion, image filtering techniques, histogram equalization, object identification, edge detection in MATLAB, Image extensions, MATLAB built-in functions, array functions, etc. Whether you’re a beginner or an experienced, embracing the knowledge will undoubtedly elevate your proficiency in digital image processing to new heights. Explore, learn, and unlock the boundless possibilities of digital imagery with this definitive tutorial.

Digital Image Processing – FAQ

Is this digital image processing tutorial is enough for beginners.

Yes, this free DIP tutorial is designed in such a way that any one can easy get an complete overview on DIP working and its core concepts.

How is Digital Image Processing used in everyday life?

Digital Image Processing is used in various applications such as medical imaging, satellite imagery analysis, facial recognition, and image editing software like Photoshop.

What are the key components of Digital Image Processing?

The key components of Digital Image Processing include image acquisition, preprocessing, segmentation, feature extraction, image enhancement, and image recognition.

How can I learn more about Digital Image Processing?

To learn more about Digital Image Processing, you can take online courses, read textbooks on the subject, attend workshops or conferences, and practice implementing algorithms using programming languages like Python or MATLAB.

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