Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-light images is of paramount importance for practical applications in the computer vision domain.To effectively address these challenges, we present a novel low-light image enhancement model, termed Spatial Consistency Retinex Network (SCRNet), which leverages the Retinex-based structure and is guided by the principle of spatial consistency.Specifically, our proposed model incorporates three levels of consistency: channel level, semantic level, and texture level, inspired by the principle of spatial consistency.These levels of consistency enable our model to adaptively enhance image features, ensuring more accurate and visually pleasing results.Extensive experimental evaluations on various low-light image datasets demonstrate that our proposed SCRNet outshines existing state-of-the-art methods, highlighting the potential of SCRNet as an effective solution for enhancing low-light images.
Retinex Meets Language: A Physics-Semantics-Guided Underwater Image Enhancement Network
State Key Laboratory of Physical Oceanography and the Faculty of Information Science and Engineering, Ocean University of China（物理海洋学国家重点实验室和中国海洋大学信息科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen university（中山大学深圳校区计算机科学与技术学院）
Low-light Image Enhancement with Retinex Decomposition in Latent Space
School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；College of Artificial Intelligence, Jiaxing University（嘉兴大学人工智能学院）；School of Artificial Intelligence at Dalian University of Technology（大连理工大学人工智能学院）；Department of Computer Science at City University of Hong Kong（香港城市大学计算机科学系）
Xi’an University of Science and Technology（西安科技大学）
A novel color image enhancement method is proposed based on Retinex to enhance color images under non-uniform illumination or poor visibility conditions. Different from the conventional Retinex algorithms, the Weighted Guided Image Filter is used as a surround function instead of the Gaussian filter to estimate the background illumination, which can overcome the drawbacks of local blur and halo artifact that may appear by Gaussian filter. To avoid color distortion, the image is converted to the HSI color model, and only the intensity channel is enhanced. Then a linear color restoration algorit…
Department of Electronic Engineering, University of Nigeria Nsukka（尼日利亚恩苏卡大学电子工程系）
This report describes the experimental results obtained using a proposed variational Retinex algorithm for controlled illumination correction. Two colour restoration and enhancement schemes of the algorithm are presented for drastically improved results. The algorithm modifies the reflectance image using global and local contrast enhancement approaches and gradually removes the residual illumination to yield highly pleasing results. The proposed algorithms are optimized by way of simultaneous perceptual quality metric (PQM) stabilization and entropy maximization for fully automated processing …
Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation
School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology（大连理工大学软件学院 & 大连理工大学-俄罗斯国际信息科学与工程学院）；School of Future Technology, Dalian University of Technology（大连理工大学未来技术学院）；Central Hospital of Dalian University of Technology（大连理工大学中心医院）；Cancer Hospital of Dalian University of Technology（大连理工大学肿瘤医院）；College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）
A Frequency-Aware Self-Supervised Learning for Ultra-Wide-Field Image Enhancement
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China（浙江工业大学信息工程学院）；Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China（中国科学院宁波材料技术与工程研究所）；Department of Eye and Vision Science, University of Liverpool, Liverpool, UK（利物浦大学眼科学与视觉科学系）；School of Biomedical Engineering, ShanghaiTech University, Shanghai, China（上海科技大学生物医学工程学院）
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；NEC Laboratories America（美国NEC实验室）；Tianyi Traffic Technology（天易交通科技）；Duke University（杜克大学）
Light 'em Up: Enabling Few-Shot Low-Light 3D Gaussian Splatting with Multi-Scale Explicit Retinex Illumination Decoupling
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；Key Laboratory of Target Cognition and Application Technology (TCAT)（目标认知与应用技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Shanghai Jiao Tong University（上海交通大学）
Pathology-Guided Virtual Staining Metric for Evaluation and Training
Department of Systems Design Engineering（系统设计工程系）；Department of Applied Mathematics（应用数学系）；Cheriton School of Computer Science（切里顿计算机科学学院）；Department of Biology（生物学系）；School of Pharmacy（药学院）
InterLight: Leveraging Intrinsic Illumination Priors for Low-Light Image Enhancement
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（武汉大学计算机学院多媒体软件国家工程研究中心）；Department of Computer Science, University of Macau（澳门大学计算机科学系）；Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence（马尔代夫穆罕默德·本·扎耶德人工智能大学计算机视觉系）；School of Information Engineering, Guangdong University of Technology（广东技术大学信息工程学院）
Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios. Recently, unsupervised approaches have been explored to eliminate the reliance on paired training data. However, they perform erratically in diverse real-world scenarios due to the absence of priors. To address this issue, we propose an unsupervised low-light image enhancement method based on an effective prior termed histogram equalization prior (HEP). Our work is inspired by the interesting observation that the feature maps of …
Wuhan University of Technology（武汉理工大学）；The Open University of Hong Kong（香港公开大学）
Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this paper, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented t…
DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal Learning
School of Cyber Science and Technology, Hubei University（湖北大学计算机科学与技术学院）；Department of Electrical Automation Design, Beijing Shougang International Engineering Technology（北京首钢国际工程技术部）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学部）；School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）
Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions
College of Aerospace Science, National University of Defense Technology（航天科学学院，国防科技大学）；Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center（人因工程重点实验室，中国航天员科研训练中心）；College of Aerospace Science and Engineering, National University of Defense Technology（航空航天工程学院，国防科技大学）
