Low-light image enhancement remains a persistent challenge in computer vision, where state-of-the-art models are often hampered by hardware constraints and computational inefficiency, particularly at high resolutions. While foundational architectures like transformers and diffusion models have advanced the field, their computational complexity limits their deployment on edge devices. We introduce ExpoMamba, a novel architecture that integrates a frequency-aware state-space model within a modified U-Net. ExpoMamba is designed to address mixed-exposure challenges by decoupling the modeling of amplitude (intensity) and phase (structure) in the frequency domain. This allows for targeted enhancement, making it highly effective for real-time applications, including downstream tasks like object detection and segmentation. Our experiments on six benchmark datasets show that ExpoMamba is up to 2-3x faster than competing models and achieves a 6.8\% PSNR improvement, establishing a new state-of-the-art in efficient, high-quality low-light enhancement. Source code: https://www.github.com/eashanadhikarla/ExpoMamba.
Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (\eg, low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (\eg, NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but te…
Automotive Software Innovation Center（汽车软件创新中心）；Institute of Intelligent Software（智能软件研究所）；Saarland University（萨尔大学）；Hangzhou Institute for Advanced Study（杭州高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）
Partial Ring Scan: Revisiting Scan Order in Vision State Space Models
College of Artificial Intelligence（人工智能学院）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Mitsubishi Electric Research Labs（三菱电机研究实验室）；Computer Science Department（计算机科学系）；University at Albany, SUNY, NY, USA（纽约州立大学阿尔巴尼分校）
DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at Once
School of Information Engineering, Guangdong University of Technology（广东技术大学信息工程学院）；Key Laboratory of Photonic Technology for Integrated Sensing and Communication, Ministry of Education of China（中国教育部长 Photonic Technology for Integrated Sensing and Communication 重点实验室）；CRRC Institute Co., Ltd.（CRRC研究院）；UBTECH Robotics Co., Ltd（UBTECH机器人有限公司）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；School of Data and Computer Science, Sun Yat-sen University（中山大学数据与计算机科学学院）
Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement
Department of Computer Science and Engineering, The Chinese University of Hong Kong（计算机科学与工程系，香港中文大学）；Snap Research；HKUST (GZ)（香港科技大学（广州））；Alibaba Tongyi Lab（阿里云联合实验室）；camera group of DJI（大疆创新相机团队）；The University of Hong Kong（香港大学）；Sun Yat-Sen University（中山大学）
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
School of EIC, Huazhong University of Science & Technology（华中科技大学电子信息与通信学院）；Institute of Artificial Intelligence, Huazhong University of Science & Technology（华中科技大学人工智能研究院）；Horizon Robotics（地平线机器人）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
LFMamba: Light Field Image Super-Resolution with State Space Model
School of Computer, Beijing Institute of Technology（北京理工大学计算机学院）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）
VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Faculty of Information Technology, Beijing University of Technology（北京工业大学信息技术学院）
Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
National University of Defense Technology（国防科技大学）；HKUST(GZ)（香港理工大学）；Qilu University of Technology（青岛科技大学）；Technical University of Munich（慕尼黑技术大学）；TeleAI, China Telecom（TeleAI，中国电信）；Beihang University（北航）
vGamba: Attentive State Space Bottleneck for efficient Long-range Dependencies in Visual Recognition
NewraLab；Beihang University（北京航空航天大学）；Beijing GoerTek Alpha Labs（北京歌尔泰克阿尔法实验室）；Imperial College London（伦敦帝国学院）；University of Electronic Science and Technology of China（电子科技大学）
MedMamba: Vision Mamba for Medical Image Classification
School of Biomedical Engineering（生物医学工程学院）；Guangzhou Medical University（广州医科大学）；School of Mathematics and Systems Science（数学与系统科学学院）；Guangdong Polytechnic Normal University（广东技术师范大学）
Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北航电子信息工程学院）；Department of Paediatrics, Cambridge University（剑桥大学儿科部）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；Department of Computing Science, Robert Gordon University（罗伯特·戈登大学计算机科学系）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家实验室）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）
