Results 261 to 270 of about 842,500 (318)

Diff-Retinex++: Retinex-Driven Reinforced Diffusion Model for Low-Light Image Enhancement

IEEE Transactions on Pattern Analysis and Machine Intelligence
This paper proposes a Retinex-driven reinforced diffusion model for low-light image enhancement, termed Diff-Retinex++, to address various degradations caused by low light.
Xunpeng Yi, Han Xu, Hao Zhang, Linfeng Tang, Jiayi Ma   +4 more
semanticscholar   +1 more source

Interpretable Optimization-Inspired Unfolding Network for Low-Light Image Enhancement

IEEE Transactions on Pattern Analysis and Machine Intelligence
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement (LLIE).
Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, Jianmin Jiang   +5 more
semanticscholar   +1 more source

Ghost Imaging in the Dark: A Multi-Illumination Estimation Network for Low-Light Image Enhancement

IEEE transactions on circuits and systems for video technology (Print)
It is well known that the diverse causes of low-light images challenge the adaptability of enhancement algorithms in uncertain environments. Most deep learning-based algorithms only learn single illuminance estimation or mapping relationship, which ...
Zhengjie Zhu, Xiaogang Yang, Ruitao Lu, Tong Shen, Tao Zhang, Siyu Wang   +5 more
semanticscholar   +1 more source

Learning With Self-Calibrator for Fast and Robust Low-Light Image Enhancement

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Neural Networks (CNNs) have shown significant success in the low-light image enhancement task. However, most of existing works encounter challenges in balancing quality and efficiency simultaneously.
Long Ma, Tengyu Ma, Chengpei Xu, Jinyuan Liu, Xin-Yue Fan, Zhongxuan Luo, Risheng Liu   +6 more
semanticscholar   +1 more source

LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models

European Conference on Computer Vision
In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, named LightenDiffusion.
Hai Jiang, Ao Luo, Xiaohong Liu, Songchen Han, Shuaicheng Liu   +4 more
semanticscholar   +1 more source

Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement

International Conference on Neural Information Processing
In the field of low-light image enhancement, both traditional Retinex methods and advanced deep learning techniques such as Retinexformer have shown distinct advantages and limitations.
Jiesong Bai, Yuhao Yin, Qiyuan He, Yuanxiang Li, Xiaofeng Zhang   +4 more
semanticscholar   +1 more source

Low-Light Image Enhancement With Semi-Decoupled Decomposition

IEEE transactions on multimedia, 2020
Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness.
Shijie Hao, Xuyang Han, Yanrong Guo, Xin Xu, Meng Wang   +4 more
semanticscholar   +1 more source

Multi-Scale Retinex Unfolding Network for Low-Light Image Enhancement

IEEE transactions on multimedia
Retinex theory-based low-light image enhancement methods have received increasing attention and achieved tremendous advancements. However, there still exist two seldom-explored issues: 1) The above methods only formally simulate the Retinex decomposition,
Huake Wang, Xingsong Hou, Jutao Li, Yadi Yan, Wenke Sun, Xin Zeng, Kaibing Zhang, Xiangyong Cao   +7 more
semanticscholar   +1 more source

NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results

2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes.
Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu, Hailong Yan, Bin Ren, Yulun Zhang, Shuhang Gu, Le Zhang, Ce Zhu, R. Timofte, Kangbiao Shi, Yixu Feng, Tao Hu, Yu Cao, Peng Wu, Yijin Liang, Yanning Zhang, Qingsen Yan, Han Zhou, Wei Dong, Yan Min, Mohab Kishawy, Jun Chen, Pengpeng Yu, Anjin Park, Seung-Soo Lee, Young-Joon Park, Zixiao Hu, Junyv Liu, Huilin Zhang, Jun Zhang, Fei Wan, Bingxin Xu, Hongzhe Liu, Cheng Xu, Weiguo Pan, Songyin Dai, Xunpeng Yi, Qinglong Yan, Yibing Zhang, Jiayi Ma, Changhui Hu, Kerui Hu, Donghang Jing, Tiesheng Chen, Zhi Jin, Hongjun Wu, Biao Huang, Haitao Ling, Jiahao Wu, Dandan Zhan, G. G. Rao, Vijayalaxmi Ashok Aralikatti, Nikhil Neelkanth Akalwadi, R. Tabib, U. Mudenagudi, Ruirui Lin, Guoxi Huang, N. Anantrasirichai, Qirui Yang, Alexandru Brateanu, Ciprian Orhei, Cosmin Ancuți, Daniel Feijoo, Juan C. Benito, Álvaro García, Marcos V. Conde, Yang Qin, Raul Balmez, Anas M. Ali, Bilel Benjdira, Wadii Boulila, Tianyi Mao, Huan Zheng, Yanyan Wei, Shengeng Tang, Dan Guo, Zhao Zhang, Sabari Nathan, K. Uma, A. Sasithradevi, B. S. Bama, S. M. M. Roomi, Ao Li, Xiangtao Zhang, Zhe Liu, Yijie Tang, Jialong Tang, Zhicheng Fu, Gong-dong Chen, Joe Nasti, John Nicholson, Zeyu Xiao, Zhuoyuan Li, Ashutosh Kulkarni, Prashant W. Patil, Santosh Kumar Vipparthi, Subrahmanyam Murala, Duan Liu, Weile Li, Hangyuan Lu, Rixian Liu, Tengfeng Wang, Jinxing Liang, Chenxin Yu, Jiancheng Huang   +105 more
semanticscholar   +1 more source

You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement

arXiv.org
Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. Most existing methods learn the mapping function between low/normal-light images by Deep Neural Networks (DNNs) on sRGB and HSV ...
Yixu Feng, Cheng Zhang, Pei Wang, Peng Wu, Qingsen Yan, Yanning Zhang   +5 more
semanticscholar   +1 more source