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Previous raw image-based low-light image enhancement methods predominantly relied on feed-forward neural networks to learn deterministic mappings from low-light to normally-exposed images.
Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
Earlier work this paper cites.
Practical poissonian-gaussian noise modeling and fitting for single-image raw-data
Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian · 2008
Earlier work this paper cites.
Optimal inversion of the anscombe transformation in low-count poisson image denoising
Markku Makitalo and Alessandro Foi · 2010
Earlier work this paper cites.
LLNet: A deep autoencoder approach to natural low-light image enhancement
Kin Gwn Lore et al · 2017
Earlier work this paper cites.
MSR-net: Low-light image enhancement using deep convolutional network
Liang Shen, Zihan Yue, Fan Feng, Quan Chen, Shihao Liu, and Jie Ma · 2017
Earlier work this paper cites.
LLCNN: A convolutional neural network for low-light image enhancement
Li Tao, Chuang Zhu, Guoqing Xiang, Yuan Li, Huizhu Jia, and Xiaodong Xie · 2017
Earlier work this paper cites.
Learning to see in the dark
Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun · 2018
Earlier work this paper cites.
Noise2noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila · 2018
Earlier work this paper cites.
Mbllen: Low-light image/video enhancement using cnns
Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim · 2018
Earlier work this paper cites.
Deep retinex decomposition for low-light enhancement
Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu · 2018
Earlier work this paper cites.
Low-light image enhancement via a deep hybrid network
Wenqi Ren, Sifei Liu, Lin Ma, Qianqian Xu, Xiangyu Xu, Xiaochun Cao, Junping Du, and Ming-Hsuan Yang · 2019
Earlier work this paper cites.
Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement
Yang Wang, Yang Cao, Zheng-Jun Zha, Jing Zhang, Zhiwei Xiong, Wei Zhang, and Feng Wu · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Srflow: Learning the super-resolution space with normalizing flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Earlier work this paper cites.
A physics-based noise formation model for extreme low-light raw denoising
Kaixuan Wei, Ying Fu, Jiaolong Yang, and Hua Huang · 2020
Earlier work this paper cites.
Low-light image and video enhancement using deep learning: A survey
Chongyi Li, Chunle Guo, Linghao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy · 2021
Cited alongside, same era.
Benchmarking low-light image enhancement and beyond
Jiaying Liu, Dejia Xu, Wenhan Yang, Minhao Fan, and Haofeng Huang · 2021
Cited alongside, same era.
Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement
Liu Risheng, Ma Long, Zhang Jiaao, Fan Xin, and Luo Zhongxuan · 2021
Cited alongside, same era.
S3rp: Self-supervised super-resolution and prediction for advection-diffusion process
Chulin Wang, Kyongmin Yeo, Xiao Jin, Andres Codas, Levente J Klein, and Bruce Elmegreen · 2021
Cited alongside, same era.
Rellie: Deep reinforcement learning for customized low-light image enhancement
Rongkai Zhang, Lanqing Guo, Siyu Huang, and Bihan Wen · 2021
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Later among the works it cites.
Accelerating score-based generative models with preconditioned diffusion sampling
Hengyuan Ma, Li Zhang, Xiatian Zhu, and Jianfeng Feng · 2022
Later among the works it cites.
Day-to-night image synthesis for training nighttime neural isps
Abhijith Punnappurath, Abdullah Abuolaim, Abdelrahman Abdelhamed, Alex Levinshtein, and Michael S Brown · 2022
Later among the works it cites.
Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
Later among the works it cites.
Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2022
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Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method
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Rethinking noise synthesis and modeling in raw denoising
Yi Zhang, Hongwei Qin, Xiaogang Wang, and Hongsheng Li · 2021
Cited alongside, same era.
Adaptive unfolding total variation network for low-light image enhancement
Chuanjun Zheng, Daming Shi, and Wentian Shi · 2021
Cited alongside, same era.
Simple baselines for image restoration
Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun · 2022
Cited alongside, same era.
Improving diffusion models for inverse problems using manifold constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, and Jong Chul Ye · 2022
Cited alongside, same era.
Abandoning the bayer-filter to see in the dark
Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, and Jiajun Shen · 2022
Cited alongside, same era.
Learnability enhancement for low-light raw denoising: Where paired real data meets noise modeling
Hansen Feng, Lizhi Wang, Yuzhi Wang, and Hua Huang · 2022
Cited alongside, same era.
Enhancing low-light images in real world via cross-image disentanglement
Lanqing Guo, Renjie Wan, Wenhan Yang, Alex Kot, and Bihan Wen · 2022
Cited alongside, same era.
Tao Wang, Kaihao Zhang, Tianrun Shen, Wenhan Luo, Bjorn Stenger, and Tong Lu · 2022
Later among the works it cites.
Low-light image enhancement with normalizing flow
Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, and Alex Kot · 2022
Later among the works it cites.
Deblurring via stochastic refinement
Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G Dimakis, and Peyman Milanfar · 2022
Later among the works it cites.
Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement
Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang · 2022
Later among the works it cites.
Snr-aware low-light image enhancement
Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia · 2022
Later among the works it cites.
gddim: Generalized denoising diffusion implicit models
Qinsheng Zhang, Molei Tao, and Yongxin Chen · 2022
Later among the works it cites.
Shadowdiffusion: When degradation prior meets diffusion model for shadow removal
Lanqing Guo, Chong Wang, Wenhan Yang, Siyu Huang, Yufei Wang, Hanspeter Pfister, and Bihan Wen · 2023
Closest in time.
Lighting every darkness in two pairs: A calibration-free pipeline for raw denoising
Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, and Chongyi Li · 2023
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Enhancing visibility in nighttime haze images using guided apsf and gradient adaptive convolution, 2023
Yeying Jin, Beibei Lin, Wending Yan, Wei Ye, Yuan Yuan, and Robby T. Tan · 2023
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Beyond learned metadata-based raw image reconstruction
Yufei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C Kot, and Bihan Wen · 2023
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Raw image reconstruction with learned compact metadata
Yufei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C Kot, and Bihan Wen · 2023
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