Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
School of Biomedical Engineering and Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction
The University of Tokyo（东京大学）；Shanghai AI Laboratory（上海人工智能实验室）；RIKEN AIP（理化学研究所人工智能研究中心）；Shanghai Jiao Tong University（上海交通大学）；Tencent Youtu Lab（腾讯优图实验室）
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
School of Engineering and Sciences, Tecnologico de Monterrey（蒙特雷理工学院工程与科学学院）；Hôpital Ambroise Paré (AP-HP)（昂布鲁瓦兹-帕雷医院（巴黎公共医疗集团））；Universidad Panamericana（泛美大学）
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）；City University of Hong Kong（香港城市大学）；Centre for Artificial Intelligence and Robotics, HKISI-CAS（中国科学院香港智能科学与健康研究院人工智能与机器人中心）；University College London（伦敦大学学院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；The University of Sydney（悉尼大学）
针对胶囊内窥镜图像不同曝光水平校正难题，提出可提示扩散 Transformer 框架 EndoUIC 及 CEC 数据集，在多数据集上优于 SOTA 并具临床应用价值。
Luminance Component Analysis for Exposure Correction
WMG, University of Warwick（华威大学WMG学院）；School of Information Science and Technology, East China University of Science and Technology（华东理工大学信息科学与工程学院）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学工程科学系生物医学工程研究所）
Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation
Nankai University（南开大学）；Beihang University（北京航空航天大学）；vivo Mobile Communication Co. Ltd.（维沃移动通信有限公司）；Guangdong University of Petrochemical Technology（广东石油化工学院）
OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University（广东省超高清沉浸媒体技术重点实验室，北京大学深圳研究生院）；King Abdullah University of Science and Technology（国王 Abdullah 科学技术大学）；The Hong Kong Polytechnic University（香港理工大学）；OPPO Research Institute（OPPO 研究院）
