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Image enhancement aims at improving the aesthetic visual quality of photos by retouching the color and tone, and is an essential technology for professional digital photography.
The retinex theory of color vision
Edwin H Land · 1977
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Bilateral filtering for gray and color images
Carlo Tomasi and Roberto Manduchi · 1998
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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
Earlier work this paper cites.
Learning photographic global tonal adjustment with a database of input/output image pairs
Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand · 2011
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Guided image filtering
Kaiming He, Jian Sun, and Xiaoou Tang · 2012
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Generalized equalization model for image enhancement
Hongteng Xu, Guangtao Zhai, Xiaolin Wu, and Xiaokang Yang · 2013
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Fast local laplacian filters: Theory and applications
Mathieu Aubry, Sylvain Paris, Samuel W Hasinoff, Jan Kautz, and Frédo Durand · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Spatio-temporally consistent color and structure optimization for multiview video color correction
Shao-Ping Lu, Beerend Ceulemans, Adrian Munteanu, and Peter Schelkens · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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LIME: Low-light image enhancement via illumination map estimation
Xiaojie Guo, Yu Li, and Haibin Ling · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Automatic photo adjustment using deep neural networks
Zhicheng Yan, Hao Zhang, Baoyuan Wang, Sylvain Paris, and Yizhou Yu · 2016
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Photographic image synthesis with cascaded refinement networks
Qifeng Chen and Vladlen Koltun · 2017
Earlier work this paper cites.
Deep bilateral learning for real-time image enhancement
Michaël Gharbi, Jiawen Chen, Jonathan T Barron, Samuel W Hasinoff, and Frédo Durand · 2017
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DSLR-quality photos on mobile devices with deep convolutional networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Unsupervised image-to-image translation networks
Ming-Yu Liu, Thomas Breuel, and Jan Kautz · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Deep photo enhancer: Unpaired learning for image enhancement from photographs with GANs
Yu-Sheng Chen, Yu-Ching Wang, Man-Hsin Kao, and Yung-Yu Chuang · 2018
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Exposure: A white-box photo post-processing framework
Flexible example-based image enhancement with task adaptive global feature self-guided network
Dario Kneubuehler, Shuhang Gu, Luc Van Gool, and Radu Timofte · 2020
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Unpaired image enhancement featuring reinforcement-learning-controlled image editing software
Satoshi Kosugi and Toshihiko Yamasaki · 2020
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DeepLPF: Deep local parametric filters for image enhancement
Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, and Gregory Slabaugh · 2020
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Learning enriched features for real image restoration and enhancement
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao · 2020
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Learning image-adaptive 3D lookup tables for high performance photo enhancement in real-time
Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang · 2020
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Yuanming Hu, Hao He, Chenxi Xu, Baoyuan Wang, and Stephen Lin · 2018
Cited alongside, same era.
Structure-revealing low-light image enhancement via robust retinex model
Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo · 2018
Cited alongside, same era.
Distort-and-Recover: Color enhancement using deep reinforcement learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo, and In So Kweon · 2018
Cited alongside, same era.
DeepISP: Toward learning an end-to-end image processing pipeline
Eli Schwartz, Raja Giryes, and Alex M Bronstein · 2018
Cited alongside, same era.
Deep retinex decomposition for low-light enhancement
Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu · 2018
Cited alongside, same era.
DeepExposure: Learning to expose photos with asynchronously reinforced adversarial learning
Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, and Bo Zhang · 2018
Cited alongside, same era.
Underexposed photo enhancement using deep illumination estimation
Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia · 2019
Cited alongside, same era.
Low-light image and video enhancement using deep learning: A survey
Chongyi Li, Chunle Guo, Ling-Hao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy · 2021
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PPR10K: A large-scale portrait photo retouching dataset with human-region mask and group-level consistency
Jie Liang, Hui Zeng, Miaomiao Cui, Xuansong Xie, and Lei Zhang · 2021
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CameraNet: A two-stage framework for effective camera isp learning
Zhetong Liang, Jianrui Cai, Zisheng Cao, and Lei Zhang · 2021
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CURL: Neural curve layers for global image enhancement
Sean Moran, Steven McDonagh, and Gregory Slabaugh · 2021
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MIEGAN: mobile image enhancement via a multi-module cascade neural network
Zhaoqing Pan, Feng Yuan, Jianjun Lei, Wanqing Li, Nam Ling, and Sam Kwong · 2021
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A comprehensive overview of image enhancement techniques
Yunliang Qi, Zhen Yang, Wenhao Sun, Meng Lou, Jing Lian, Wenwei Zhao, Xiangyu Deng, and Yide Ma · 2021
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Real-time image enhancer via learnable spatial-aware 3d lookup tables
Tao Wang, Yong Li, Jingyang Peng, Yipeng Ma, Xian Wang, Fenglong Song, and Youliang Yan · 2021
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Very lightweight photo retouching network with conditional sequential modulation
Yihao Liu, Jingwen He, Xiangyu Chen, Zhengwen Zhang, Hengyuan Zhao, Chao Dong, and Yu Qiao · 2022
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Toward fast, flexible, and robust low-light image enhancement
Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo · 2022
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AdaInt: Learning adaptive intervals for 3D lookup tables on real-time image enhancement
Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, and Ying Chen · 2022
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