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• 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]

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Image processing articles within Scientific Reports

Article 11 May 2024 | Open Access

Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography

• Kanato Masayoshi
• , Yusaku Katada
•  &  Toshihide Kurihara

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification

• Pranav Singh
• , Raviteja Chukkapalli
•  &  Jacopo Cirrone

Article 09 May 2024 | Open Access

A dual-branch selective attention capsule network for classifying kiwifruit soft rot with hyperspectral images

• Zhiqiang Guo
• , Yingfang Ni
•  &  Yunliu Zeng

Article 07 May 2024 | Open Access

Long-term stimulation by implanted pacemaker enables non-atrophic treatment of bilateral vocal fold paresis in a human-like animal model

• Kassandra Walluks
• , Bianca Hoffmann
•  &  Dirk Arnold

Article 30 April 2024 | Open Access

Segmentation of liver CT images based on weighted medical transformer model

• , Hai Zhang
•  &  Rui Wang

Article 24 April 2024 | Open Access

MRI radiomics in head and neck cancer from reproducibility to combined approaches

• , Stefano Cavalieri
•  &  Luca Mainardi

Article 22 April 2024 | Open Access

A comparative analysis of pairwise image stitching techniques for microscopy images

• Fatemeh Sadat Mohammadi
• , Seyyed Erfan Mohammadi
•  &  Hasti Shabani

Article 19 April 2024 | Open Access

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

• Thomas Louis
• , François Lucia
•  &  Roland Hustinx

Article 18 April 2024 | Open Access

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

• , Taymaz Akan
•  &  Mohammad A. N. Bhuiyan

Article 16 April 2024 | Open Access

Automated quantification of avian influenza virus antigen in different organs

• Maria Landmann
• , David Scheibner
•  &  Reiner Ulrich

Article 08 April 2024 | Open Access

A novel vector field analysis for quantitative structure changes after macular epiretinal membrane surgery

• Seok Hyun Bae
• , Sojung Go
•  &  Sang Jun Park

Article 05 April 2024 | Open Access

Advanced disk herniation computer aided diagnosis system

• Maad Ebrahim
• , Mohammad Alsmirat
•  &  Mahmoud Al-Ayyoub

Article 28 March 2024 | Open Access

Brain temperature and free water increases after mild COVID-19 infection

• Ayushe A. Sharma
• , Rodolphe Nenert
•  &  Jerzy P. Szaflarski

Article 26 March 2024 | Open Access

High-capacity data hiding for medical images based on the mask-RCNN model

• Hadjer Saidi
• , Okba Tibermacine
•  &  Ahmed Elhadad

Article 25 March 2024 | Open Access

Integrated image and location analysis for wound classification: a deep learning approach

• , Tirth Shah
•  &  Zeyun Yu

Article 21 March 2024 | Open Access

A number sense as an emergent property of the manipulating brain

• Neehar Kondapaneni
•  &  Pietro Perona

Article 16 March 2024 | Open Access

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model

• Lorenz A. Kapsner
• , Lukas Folle
•  &  Sebastian Bickelhaupt

Article 14 March 2024 | Open Access

Dual ensemble system for polyp segmentation with submodels adaptive selection ensemble

• , Kefeng Fan
•  &  Kaijie Jiao

Article 11 March 2024 | Open Access

Generalizable disease detection using model ensemble on chest X-ray images

• Maider Abad
• , Jordi Casas-Roma
•  &  Ferran Prados

Article 08 March 2024 | Open Access

Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio

• Patrick Thiam
• , Christopher Kloth
•  &  Hans A. Kestler

Article 05 March 2024 | Open Access

Brain volume measured by synthetic magnetic resonance imaging in adult moyamoya disease correlates with cerebral blood flow and brain function

• Kazufumi Kikuchi
• , Osamu Togao
•  &  Kousei Ishigami

Article 04 March 2024 | Open Access

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology

• Okyaz Eminaga
• , Mahmoud Abbas
•  &  Olaf Bettendorf

Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies

• Renaldas Augulis
• , Allan Rasmusson
•  &  Arvydas Laurinavicius

Opportunistic screening with multiphase contrast-enhanced dual-layer spectral CT for osteoblastic lesions in prostate cancer compared with bone scintigraphy

• Ming-Cheng Liu
• , Chi-Chang Ho
•  &  Yi-Jui Liu

Article 02 March 2024 | Open Access

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

• Ahmed Elhadad
• , Mona Jamjoom
•  &  Hussein Abulkasim

Article 29 February 2024 | Open Access

Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort

• Minseon Gwak
• , Jong Pil Yun
•  &  Chena Lee

End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure

• Romain Fernandez
• , Loïc Le Cunff
•  &  Cédric Moisy

Article 27 February 2024 | Open Access

An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism

• , Handing Song
•  &  Zhan Wang

Article 26 February 2024 | Open Access

Quantifying mangrove carbon assimilation rates using UAV imagery

• Javier Blanco-Sacristán
• , Kasper Johansen
•  &  Matthew F. McCabe

Article 24 February 2024 | Open Access

Iterative pseudo balancing for stem cell microscopy image classification

• Adam Witmer
•  &  Bir Bhanu

Article 22 February 2024 | Open Access

Deep learning-based, fully automated, pediatric brain segmentation

• Min-Jee Kim
• , EunPyeong Hong
•  &  Tae-Sung Ko

Article 21 February 2024 | Open Access

Correction of high-rate motion for photoacoustic microscopy by orthogonal cross-correlation

• , Qiuqin Mao
•  &  Xiaojun Liu

Article 20 February 2024 | Open Access

ERCP-Net: a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network

•  &  Yannan Xu

A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model

• Xiu’yan Zhao
• , Yu’xiang He
•  &  Kai’xing Zhang

Article 16 February 2024 | Open Access

Stripe noise removal in conductive atomic force microscopy

• , Jan Rieck
•  &  Michael H. F. Wilkinson

Article 13 February 2024 | Open Access

Automatic enhancement preprocessing for segmentation of low quality cell images

•  &  Kazuhiro Hotta

Article 09 February 2024 | Open Access

An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes

• Timothy K. Chung
• , Pete H. Gueldner
•  &  David A. Vorp

Article 08 February 2024 | Open Access

Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma

• Shamimeh Ahrari
• , Timothée Zaragori
•  &  Antoine Verger

Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical ViT

• , Shivam Kalra
•  &  Mohammad Saleh Miri

Article 03 February 2024 | Open Access

YOLOX target detection model can identify and classify several types of tea buds with similar characteristics

• Mengdao Yang
• , Weihao Yuan
•  &  Gaojian Xu

Phenotypic characterization of liver tissue heterogeneity through a next-generation 3D single-cell atlas

• Dilan Martínez-Torres
• , Valentina Maldonado
•  &  Fabián Segovia-Miranda

Article 30 January 2024 | Open Access

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

• Amir Reza Naderi Yaghouti
• , Hamed Zamanian
•  &  Ahmad Shalbaf

Research on improved black widow algorithm for medical image denoising

•  &  Lina Zhang

Article 25 January 2024 | Open Access

Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT

• Krzysztof Psiuk-Maksymowicz
• , Damian Borys
•  &  Ryszard A. Bialecki

Article 23 January 2024 | Open Access

Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue

• Zhongtian Shao
• , Lane B. Buchanan
•  &  Jessica L. Prodger

Task design for crowdsourced glioma cell annotation in microscopy images

• Svea Schwarze
• , Nadine S. Schaadt
•  &  Friedrich Feuerhake

Article 20 January 2024 | Open Access

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

• Margherita Burattini
• , Francesco Paolo Lo Muzio
•  &  Michele Miragoli

Article 19 January 2024 | Open Access

Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19

• Lucas Scardua-Silva
• , Beatriz Amorim da Costa
•  &  Clarissa Lin Yasuda

Article 18 January 2024 | Open Access

Validation of reliability, repeatability and consistency of three-dimensional choroidal vascular index

• , Yifan Bai
•  &  Qingli Shang

Integrated image and sensor-based food intake detection in free-living

• Tonmoy Ghosh
•  &  Edward Sazonov

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Research on Image Processing Technology Based on Artificial Intelligence Algorithm

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• Jiaqi Xu 5  

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 156))

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• International Conference on Cognitive based Information Processing and Applications

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Artificial intelligence algorithm can optimize the traditional image processing technology, so that the technology can give more accurate and high-quality results. This paper mainly introduces the basic concept of artificial intelligence algorithm and its application advantages in image processing and then establishes the artificial intelligence image processing technology system. The research proves that the image processing technology supported by artificial intelligence algorithm can give higher quality results, indicating that the algorithm has higher application value in image processing.

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Guangxi University, Nanning, 530004, Guangx, China

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Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

Bernard J. Jansen

School of Economics and Management, Changzhou Institute of Mechatronic Technology, Changzhou, China

Qingyuan Zhou

School of Computer Science and Cyberspace Security, Hainan University, Haikou, Hainan, China

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Xu, J. (2023). Research on Image Processing Technology Based on Artificial Intelligence Algorithm. In: Jansen, B.J., Zhou, Q., Ye, J. (eds) Proceedings of the 2nd International Conference on Cognitive Based Information Processing and Applications (CIPA 2022). CIPA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 156. Springer, Singapore. https://doi.org/10.1007/978-981-19-9376-3_72

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DOI : https://doi.org/10.1007/978-981-19-9376-3_72

Published : 09 April 2023

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-9375-6

Online ISBN : 978-981-19-9376-3

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