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Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) restoring SR image based on estimated kernel.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David R. Martin, Charless C. Fowlkes, Doron Tal, and Jitendra Malik · 2001
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Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen O. Egiazarian · 2007
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Image upsampling via imposed edge statistics
Raanan Fattal · 2007
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Super-resolution from a single image
Daniel Glasner, Shai Bagon, and Michal Irani · 2009
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On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 2010
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie-Line Alberi-Morel · 2012
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Nonparametric blind super-resolution
Tomer Michaeli and Michal Irani · 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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Weighted nuclear norm minimization with application to image denoising
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
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Accelerating the super-resolution convolutional neural network
Chao Dong, Chen Change Loy, and Xiaoou Tang · 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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Sketch-based manga retrieval using manga109 dataset
Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa · 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
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte 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.
Learning deep cnn denoiser prior for image restoration
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang · 2017
Cited alongside, same era.
Fast, accurate, and lightweight super-resolution with cascading residual network
Wide activation for efficient and accurate image super-resolution
Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, and Thomas S. Huang · 2018
Later among the works it cites.
Learning a single convolutional super-resolution network for multiple degradations
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
Later among the works it cites.
Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
Later among the works it cites.
Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
Later among the works it cites.
Blind super-resolution kernel estimation using an internal-gan
Sefi Bell-Kligler, Assaf Shocher, and Michal Irani · 2019
Later among the works it cites.
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Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 2018
Cited alongside, same era.
To learn image super-resolution, use a gan to learn how to do image degradation first
Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos · 2018
Cited alongside, same era.
Deep back-projection networks for super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Deblurring images via dark channel prior
Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang · 2018
Cited alongside, same era.
"zero-shot" super-resolution using deep internal learning
Assaf Shocher, Nadav Cohen, and Michal Irani · 2018
Cited alongside, same era.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yi-Hao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaoou Tang · 2018
Cited alongside, same era.
Blind image super-resolution with spatially variant degradations
Victor Cornillere, Abdelaziz Djelouah, Wang Yifan, Olga Sorkine-Hornung, and Christopher Schroers · 2019
Later among the works it cites.
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.
Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
Later among the works it cites.
Meta-sr: A magnification-arbitrary network for super-resolution
Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, and Jian Sun · 2019
Later among the works it cites.
Deep plug-and-play super-resolution for arbitrary blur kernels
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 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
Closest in time.
Unified dynamic convolutional network for super-resolution with variational degradations
Yu-Syuan Xu, Shou-Yao Roy Tseng, Yu Hung Tseng, Hsien-Kai Kuo, and Yi-Min Tsai · 2020
Closest in time.
Deep unfolding network for image super-resolution
Kai Zhang, Luc Van Gool, and Radu Timofte · 2020
Closest in time.