Results 271 to 280 of about 850,785 (317)

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

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

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

Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement

ACM Multimedia
Ultra-high-definition (UHD) technology has attracted widespread attention due to its exceptional visual quality, but it also poses new challenges for low-light image enhancement (LLIE) techniques.
Wenbin Zou, Hongxia Gao, Weipeng Yang, Tongtong Liu   +3 more
semanticscholar   +1 more source

Low-Light Stereo Image Enhancement

IEEE Transactions on Multimedia, 2023
Jie Huang, Xueyang Fu, Zeyu Xiao, Feng Zhao, Zhiwei Xiong   +4 more
openaire   +1 more source

Wakeup-Darkness: When Multimodal Meets Unsupervised Low-Light Image Enhancement

ACM Trans. Multim. Comput. Commun. Appl.
Low-light image enhancement is a crucial visual task, and many unsupervised methods overlook the degradation of visible information in low-light scenes, adversely affecting the fusion of complementary information and hindering the generation of ...
Xiaofeng Zhang, Zishan Xu, Hao Tang, Chaochen Gu, Wei Chen, Abdulmotaleb El Saddik   +5 more
semanticscholar   +1 more source

Unsupervised Low-light Image Enhancement with Generated Low-light Image Pairs

Poster Volume Ⅰ The 2024 Twentieth International Conference on Intelligent Computing August 5-8, 2024 Tianjin, China
Low-light image enhancement aims to improve the perception of images captured under low-light conditions. Many previous unsupervised methods rely solely on information from a single image and multiple priors for enhancement. However, the information within a single image is limited, and designing suitable priors could be challenging.
openaire   +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

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

Low-light Image Enhancement via Dual Reflectance Estimation

Journal of Scientific Computing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fan Jia, Tiange Wang, Tieyong Zeng
openaire   +1 more source