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Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images
Michael Elad and Arie Feuer · 1997
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Super-resolution from a single image
Daniel Glasner, Shai Bagon, and Michal Irani · 2009
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On bayesian adaptive video super resolution
Ce Liu and Deqing Sun · 2013
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Nonparametric blind super-resolution
Tomer Michaeli and Michal Irani · 2013
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Making a completely blind image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2013
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Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 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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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 Kingma and Jimmy Ba · 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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Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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A holistic approach to cross-channel image noise modeling and its application to image denoising
Seonghyeon Nam, Youngbae Hwang, Yasuyuki Matsushita, and Seon Joo Kim · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 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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Deep laplacian pyramid networks for fast and accurate super-resolution
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Cited alongside, same era.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Cited alongside, same era.
Enhancenet: Single image super-resolution through automated texture synthesis
Mehdi SM Sajjadi, Bernhard Schölkopf, and Michael Hirsch · 2017
Cited alongside, same era.
Jpeg-resistant adversarial images
Richard Shin and Dawn Song · 2017
Cited alongside, same era.
Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
Later among the works it cites.
Frequency separation for real-world super-resolution
Manuel Fritsche, Shuhang Gu, and Radu Timofte · 2019
Later among the works it cites.
Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
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Unsupervised learning for real-world super-resolution
Andreas Lugmayr, Martin Danelljan, and Radu Timofte · 2019
Later among the works it cites.
Path-restore: Learning network path selection for image restoration
Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, and Chen Change Loy · 2019
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Semantic understanding of scenes through the ade20k dataset
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Image super-resolution via deep recursive residual network
Ying Tai, Jian Yang, and Xiaoming Liu · 2017
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, et al · 2017
Cited alongside, same era.
The 2018 pirm challenge on perceptual image super-resolution
Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, and Lihi Zelnik-Manor · 2018
Cited alongside, same era.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
Cited alongside, same era.
Deep backprojection networks for super-resolution
Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita · 2018
Cited alongside, same era.
Non-local recurrent network for image restoration
Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, and Thomas S Huang · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
Later among the works it cites.
Kernel modeling super-resolution on real low-resolution images
Ruofan Zhou and Sabine Susstrunk · 2019
Later among the works it cites.
Real-world super-resolution via kernel estimation and noise injection
Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang · 2020
Later among the works it cites.
Estimating generalized gaussian blur kernels for out-of-focus image deblurring
Yu-Qi Liu, Xin Du, Hui-Liang Shen, and Shu-Jie Chen · 2020
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Unfolding the alternating optimization for blind super resolution
Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, and Tieniu Tan · 2020
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A u-net based discriminator for generative adversarial networks
Edgar Schonfeld, Bernt Schiele, and Anna Khoreva · 2020
Later among the works it cites.
Component divide-and-conquer for real-world image super-resolution
Pengxu Wei, Ziwei Xie, Hannan Lu, ZongYuan Zhan, Qixiang Ye amd Wangmeng Zuo, and Liang Lin · 2020
Later among the works it cites.
Glean: Generative latent bank for large-factor image super-resolution
Kelvin C.K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, and Chen Change Loy · 2021
Closest in time.
Blind image super-resolution: A survey and beyond
Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiaoand, and Chao Dong · 2021
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Diffjpeg
Michael R Lomnitz · 2021
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Unsupervised degradation representation learning for blind super-resolution
Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo · 2021
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Towards real-world blind face restoration with generative facial prior
Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan · 2021
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Fine-grained attention and feature-sharing generative adversarial networks for single image super-resolution
Yitong Yan, Chuangchuang Liu, Changyou Chen, Xianfang Sun, Longcun Jin, Peng Xinyi, and Xiang Zhou · 2021
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Designing a practical degradation model for deep blind image super-resolution
Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte · 2021
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