A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a low-complexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets to demonstrate the superior performance of NATLE.
Underwater Image Enhancement Based on Structure-Texture Reconstruction
School of Automation and Electrical Engineering, Shenyang Ligong University（沈阳理工大学自动化与电气工程学院）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；School of Electronic and Information Engineering, Liaoning Technical University（辽宁工程技术大学电子与信息工程学院）
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Academy for Engineering and Technology, Fudan University（复旦大学工程技术研究院）
EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China（中国科学技术大学，脑启发智能感知与认知教育部重点实验室）；Midea Group（美的集团）
GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
1 Graduate School of Engineering, Tohoku University, Sendai, Japan 2 Dept. of Radiology \& Biomedical Imaging, Yale University, New Haven, CT, USA 3 Dept. of Computer Science；Information Engineering, National Taiwan University, Taipei, Taiwan 4 Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan 5 Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
Northwestern Polytechnical University（西北工业大学）；Xi’an University of Architecture and Technology（西安建筑科技大学）；Xi’an Institute of Optics and Precision Mechanics of CAS（中国科学院西安光学精密机械研究所）
Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform
East China University of Science and Technology（华东理工大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）
Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement
Shaanxi Key Laboratory for Network Computing and Security Technology（陕西网络计算与安全技术重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Xi'an University of Technology（西安理工大学）
Structure-guided Diffusion Transformer for Low-Light Image Enhancement
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合国家科学中心人工智能研究院）；Anhui University（安徽大学）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动技术学院）；State Key Laboratory of Automotive Safety and Energy, Tsinghua University（清华大学汽车安全与能源国家重点实验室）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；Department of Computer and Network Engineering, The University of Electro-Communications（电通大学计算机与网络工程系）
Multi-scale Frequency Enhancement Network for Blind Image Deblurring
The Institute of Systems Engineering, Academy of Military Science（军事科学院系统工程研究所）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University（江南大学江苏省模式识别与计算智能工程实验室）；College of Artificial Intelligence, China University of Petroleum(beijing)（中国石油大学（北京）人工智能学院）；Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum(beijing)（中国石油大学（北京）北京市石油数据挖掘重点实验室）
Anisotropic Diffusion for Details Enhancement in Multi-Exposure Image Fusion
Baddi University of Emerging Sciences and Technology（巴迪新兴科学与技术大学）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Jaypee University of Information Technology（贾佩信息技术大学）
LightQANet: Quantized and Adaptive Feature Learning for Low-Light Image Enhancement
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机视觉研究院）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学人工智能与先进计算学院）；School of Mathematics and Statistics, Changsha University of Science and Technology（长沙理工大学数学与统计学院）；School of Computer Science, Sichuan Normal University（四川师范大学计算机科学学院）；Department of Computer Science, University of Nottingham Ningbo China（宁波大学计算机科学系）
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University（计算机视觉研究所，计算机科学与软件工程学院，深圳大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（社会人工智能与机器人研究所）；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室，深圳大学）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（人工智能与高级计算学院，西安交通大学-利物浦大学）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（大数据系统计算技术国家工程实验室，深圳大学）；Department of Electrical & Computer Engineering, University of Alberta（电气与计算机工程系，阿尔伯塔大学）
Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；DAMO Academy, Alibaba Group（阿里云达摩院）；Union Hospital, Huazhong University of Science and Technology（华中科技大学同济医院）；Xiangya Hospital, Central South University（中南大学湘雅医院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Hupan Lab, Hangzhou 310023, China（沪幻实验室）
LP$^{2}$DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
