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Super resolution (SR) methods typically assume that the low-resolution (LR) image was downscaled from the unknown high-resolution (HR) image by a fixed 'ideal' downscaling kernel (e.g.
Blind super-resolution using a learning-based approach
Isabelle Begin and Frank P. Ferrie · 2004
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Patch based blind image super resolution
Qiang Wang, Xiaoou Tang, and Harry Shum · 2005
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Psf estimation using sharp edge prediction
Neel Joshi, Richard Szeliski, and David J. Kriegman · 2008
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Super-resolution from a single image
Daniel Glasner, Shai Bagon, and Michal Irani · 2009
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A soft map framework for blind super-resolution image reconstruction
Yu He, Kim-Hui Yap, Li Chen, and Lap-Pui Chau · 2009
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Image and video upscaling from local self-examples
Gilad Freedman and Raanan Fattal · 2011
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Single image super-resolution using gaussian process regression
He He and Wan-Chi Siu · 2011
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Internal statistics of a single natural image
Maria Zontak and Michal Irani · 2011
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Nonparametric blind super-resolution
T. Michaeli and M. Irani · 2013
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Accurate blur models vs. image priors in single image super-resolution
Alexander Apartsin Boaz Nadler Anat levin Netalee Efrat, Daniel Glasner · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, 2013
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2013
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Learning a deep convolutional network for image super-resolution
Kaiming He Xiaoou Tang Chao Dong, Chen Change Loy · 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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A+: Adjusted anchored neighborhood regression for fast super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 2014
Cited alongside, same era.
The Loss Surfaces of Multilayer Networks
Anna Choromanska, MIkael Henaff, Michael Mathieu, Gerard Ben Arous, and Yann LeCun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Conditioned regression models for non-blind single image super-resolution
G. Riegler, S. Schulter, M. Rüther, and H. Bischof · 2015
Cited alongside, same era.
Identity matters in deep learning
Moritz Hardt and Tengyu Ma · 2016
Cited alongside, same era.
Deep learning without poor local minima
On the optimization of deep networks: Implicit acceleration by overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan · 2018
Later among the works it cites.
Deep back-projection networks for super-resolution
Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Zero-shot super-resolution using deep internal learning
Assaf Shocher, Nadav Cohen, and Michal Irani · 2018
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Ntire 2018 challenge on single image super-resolution: Methods and results
Radu Timofte, Shuhang Gu, Jiqing Wu, Luc Van Gool, Lei Zhang, Ming-Hsuan Yang, Muhammad Haris, et al · 2018
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Deep poly-dense network for image superresolution
Wang Xintao, Yu Ke, Hui Tak-Wai, Dong Chao, Lin Liang, and Change Loy Chen · 2018
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Kenji Kawaguchi · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Cited alongside, same era.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Cited alongside, same era.
Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 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.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 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.
Later among the works it cites.
A fully progressive approach to single-image super-resolution
W. Yifan, F. Perazzi, B. McWilliams, A. Sorkine-Hornung, O Sorkine-Hornung, and C. Schroers · 2018
Later among the works it cites.
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.
Toward real-world single image super-resolution: A new benchmark and a new model, 2019
Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang · 2019
Closest in time.
Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
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Ingan: Capturing and remapping the “dna” of a natural image
Assaf Shocher, Shai Bagon, Phillip Isola, and Michal Irani · 2019
Closest in time.