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Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers.
Lightness and retinex theory
Edwin H Land and John J McCann · 1971
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Jan C Willems · 1986
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Observer-based robust controller design for a linear system with time-varying perturbations
Shyi-Kae Yang and Chieh-Li Chen · 1997
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Blind inverse gamma correction
Hany Farid · 2001
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
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A dynamic histogram equalization for image contrast enhancement
Mohammad Abdullah-Al-Wadud, Md. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae · 2007
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Novel lmi conditions for observer-based stabilization of lipschitzian nonlinear systems and uncertain linear systems in discrete-time
Salim Ibrir and Sette Diopt · 2008
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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LLNet: A deep autoencoder approach to natural low-light image enhancement
Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Learning a deep single image contrast enhancer from multi-exposure images
Jianrui Cai, Shuhang Gu, and Lei Zhang · 2018
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MBLLEN: low-light image/video enhancement using cnns
Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Deep retinex decomposition for low-light enhancement
Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu · 2018
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Seeing motion in the dark
Chen Chen, Qifeng Chen, Minh N. Do, and Vladlen Koltun · 2019
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Getting to know low-light images with the exclusively dark dataset
Yuen Peng Loh and Chee Seng Chan · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Underexposed photo enhancement using deep illumination estimation
Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia · 2019
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Kindling the darkness: A practical low-light image enhancer
Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo · 2019
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Zero-reference deep curve estimation for low-light image enhancement
Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong · 2020
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
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Interpreting super-resolution networks with local attribution maps
Jinjin Gu and Chao Dong · 2021
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Enlightengan: Deep light enhancement without paired supervision
Retinexformer: One-stage retinex-based transformer for low-light image enhancement
Yuanhao Cai, Hao Bian, Jing Lin, Haoqian Wang, Radu Timofte, and Yulun Zhang · 2023
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Hungry hungry hippos: Towards language modeling with state space models
Daniel Y. Fu, Tri Dao, Khaled Kamal Saab, Armin W. Thomas, Atri Rudra, and Christopher Ré · 2023
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Learning a simple low-light image enhancer from paired low-light instances
Zhenqi Fu, Yan Yang, Xiaotong Tu, Yue Huang, Xinghao Ding, and Kai-Kuang Ma · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Llformer: An efficient and real-time lidar lane detection method based on transformer
Haoxiang Jie, Xinyi Zuo, Jian Gao, Wei Liu, Jun Hu, and Shuai Cheng · 2023
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Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang · 2021
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Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement
Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo · 2021
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Seeing dynamic scene in the dark: A high-quality video dataset with mechatronic alignment
Ruixing Wang, Xiaogang Xu, Chi-Wing Fu, Jiangbo Lu, Bei Yu, and Jiaya Jia · 2021
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Sparse gradient regularized deep retinex network for robust low-light image enhancement
Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu · 2021
Cited alongside, same era.
On the parameterization and initialization of diagonal state space models
Albert Gu, Karan Goel, Ankit Gupta, and Christopher Ré · 2022
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2022
Cited alongside, same era.
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 · 2022
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Large selective kernel network for remote sensing object detection
Yuxuan Li, Qibin Hou, Zhaohui Zheng, Ming-Ming Cheng, Jian Yang, and Xiang Li · 2023
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Unsupervised low-light video enhancement with spatial-temporal co-attention transformer
Xiaoqian Lv, Shengping Zhang, Chenyang Wang, Weigang Zhang, Hongxun Yao, and Qingming Huang · 2023
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Diff-retinex: Rethinking low-light image enhancement with A generative diffusion model
Xunpeng Yi, Han Xu, Hao Zhang, Linfeng Tang, and Jiayi Ma · 2023
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Mambair: A simple baseline for image restoration with state-space model
Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia · 2024
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Localmamba: Visual state space model with windowed selective scan
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Di-retinex: Digital-imaging retinex theory for low-light image enhancement
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