A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Northwestern Polytechnical University（西北工业大学）；Space and Upper Atmosphere Research Commission（空间与高层大气研究委员会）；National University of Computer and Emerging Sciences（国立计算机与新兴科学大学）
Syed Muhammad Arsalan Bashir, Yi Wang, Mahrukh Khan, Yilong Niu
Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；University of Cincinnati（辛辛那提大学）
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervised registration categories, for example, fully supervised, dual supervised, and weakly supervised registration. Next, similarity-based as well as generative adversarial network (GAN)-based registration are presented as part of unsupervised registrat…
A Comprehensive Review on Noise Control of Diffusion Model
University of Pittsburgh（匹兹堡大学）；Northeastern University（东北大学）；Columbia University（哥伦比亚大学）；Northwestern University（西北大学）；University of California, Berkeley（加州大学伯克利分校）
A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Khalifa University（哈利法大学）；National University of Sciences and Technology（巴基斯坦国立科技大学）；Margalla Institute of Health Sciences（马尔加拉健康科学学院）；Abu Dhabi University（阿布扎比大学）；Weill Cornell Medicine-Qatar（威尔康奈尔医学院卡塔尔分校）
National Institute of Technology, Warangal（瓦朗加尔国家理工学院）；Aarhus University（奥胡斯大学）
Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting th…
Southwest Jiaotong University（西南交通大学）；Mississippi State University（密西西比州立大学）；University of New South Wales（新南威尔士大学）；University of Extremadura（埃斯特雷马杜拉大学）
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including con…
The raising availability of 3D cameras and dramatic improvement of computer vision algorithms in the recent decade, accelerated the research of automatic movement assessment solutions. Such solutions can be implemented at home, using affordable equipment and dedicated software. In this paper, we divide the movement assessment task into secondary tasks and explain why they are needed and how they can be addressed. We review the recent solutions for automatic movement assessment from skeleton videos, comparing them by their objectives, features, movement domains and algorithmic approaches. In ad…
Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）；Chengdu University of Information Technology（成都信息工程大学）
Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems int…
National Heart and Lung Institute, Faculty of Medicine, Imperial College London（帝国理工学院医学院国家心肺研究所）；Department of Bioengineering, Faculty of Engineering, Imperial College London（帝国理工学院工程系生物工程系）；Royal Brompton Hospital（皇家布朗普顿医院）
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure …
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
Barrett Hodgson University（巴雷特霍奇森大学）；Quest International University（奎斯特国际大学）；NED University of Engineering and technology（NED工程技术大学）；Université Claude Bernard Lyon1（里昂第一大学）
Dynamic attention mechanism and global modeling ability make Transformer show strong feature learning ability. In recent years, Transformer has become comparable to CNNs methods in computer vision. This review mainly investigates the current research progress of Transformer in image and video applications, which makes a comprehensive overview of Transformer in visual learning understanding. First, the attention mechanism is reviewed, which plays an essential part in Transformer. And then, the visual Transformer model and the principle of each module are introduced. Thirdly, the existing Transf…
University of Wollongong（卧龙岗大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织 Data61）；University of Sydney（悉尼大学）
Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, th…
Nanjing University of Science and Technology（南京理工大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；The University of Adelaide（阿德莱德大学）；The University of Queensland（昆士兰大学）；Hunan University（湖南大学）；Sungkyunkwan University（成均馆大学）
This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attacker's capability and affected stage of the machine learning pipeline, the attack surfaces are recognized to be wide and then formalized into six categorizations: code poisoning, outsourcing, pretrained, data collection, collaborative learning and post-deployment. Accordingly, attacks under each categorization are combed. The countermeasures are categorized into four general classes: blind backdoor removal, offline backdoor inspection, online backdo…
University of New South Wales（新南威尔士大学）；University of Luebeck（吕贝克大学）；Northeastern University（东北大学）；China Medical University（中国医科大学）
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentati…
Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review
Meituan（美团）；Shanghai Jiao Tong University（上海交通大学）；Beijing Institute of Technology（北京理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Northeastern University（东北大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Nanjing University of Science and Technology（南京理工大学）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）
Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and …
A Comprehensive Review of Knowledge Distillation in Computer Vision
Bharti School of Telecommunication Technology and management, Indian Institute of Technology New Delhi（印度理工学院新德里分校巴蒂电信技术与管理学院）；National Institute of Technology Srinagar（斯利那加国家理工学院）
Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Shanghai for Science and Technology（上海理工大学）
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey…
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network. This technique has been increasingly applied in deep neural network learning systems for various computer vision applications recently. There is a consensus that deep supervision helps improve neural network performance by alleviating the gradient vanishing problem, as one of the many strengths of deep supervision. Besides, in different computer vision applications, deep supervision can be applied in different ways. How to make the most use of deep su…
Northeastern University（东北大学）；Hunan First Normal University（湖南第一师范学院）；University of Luebeck（吕贝克大学）
Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused …
Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of …
Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives
School of Computer Science（计算机科学学院）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）；Okinawa Institute of Science and Technology (OIST)（冲绳科学技术大学院大学）
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
Arab American University（阿拉伯美国大学）；Columbia University（哥伦比亚大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Al-Ahliyya Amman University（阿赫利耶阿曼大学）；Queensland University of Technology（昆士兰科技大学）
Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives
School of Computer Science, Universiti Sains Malaysia（马来西亚理科大学计算机科学学院）；School of Engineering, College of Technology and Business, Guangzhou（广州工商学院工程学院）
