Abandoning the Bayer-Filter to See in the Dark

8 March 2022

Papers citing "Abandoning the Bayer-Filter to See in the Dark"

10 / 10 papers shown

Title
A review of advancements in low-light image enhancement using deep learning Fangxue Liu Lei Fan 37 0 0 09 May 2025
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image Enhancement Yi Li Zhiyuan Zhang Jiangnan Xia Jianghan Cheng Qilong Wu Junwei Li Yibin Tian Hui Kong DiffM 39 0 0 07 May 2025
Brightness Perceiving for Recursive Low-Light Image Enhancement H. Wang Long Peng Yuejin Sun Zengyu Wan Yang Wang Yang Cao 65 12 0 03 Apr 2025
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement Xianmin Chen Peiliang Huang Xiaoxu Feng Dingwen Zhang Longfei Han J. Han Mamba 68 1 0 03 Jan 2025
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion Xinyi Liu Qian Zhao Jie-Kai Liang Huiyu Zeng Deyu Meng Lei Zhang 30 0 0 14 Dec 2023
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models Qiang Wen Yazhou Xing Zhefan Rao Qifeng Chen DiffM 30 0 0 02 Dec 2023
Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond Shen Zheng Yi Ma Jinqian Pan Chang-Tien Lu Gaurav Gupta 27 21 0 21 Dec 2022
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising Weihao Zhuang T. Hascoet R. Takashima T. Takiguchi 6 0 0 29 Nov 2022
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition Yihong Dong Ying Peng Muqiao Yang Songtao Lu Qingjiang Shi 38 8 0 05 Jun 2021
Learning to See in the Dark Cheng Chen Qifeng Chen Jia Xu V. Koltun 168 1,157 0 04 May 2018