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It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
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What is the space of camera response functions?
Michael D Grossberg and Shree K Nayar · 2003
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High-quality linear interpolation for demosaicing of bayer-patterned color images
Henrique S Malvar, Li-wei He, and Ross Cutler · 2004
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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Nonlocally centralized sparse representation for image restoration
Weisheng Dong, Lei Zhang, Guangming Shi, and Xin Li · 2013
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Accurate blur models vs. image priors in single image super-resolution
Netalee Efrat, Daniel Glasner, Alexander Apartsin, Boaz Nadler, and Anat Levin · 2013
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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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Gaussian assumption: The least favorable but the most useful [lecture notes]
Sangwoo Park, Erchin Serpedin, and Khalid Qaraqe · 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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A statistical prediction model based on sparse representations for single image super-resolution
Tomer Peleg and Michael Elad · 2014
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A+: Adjusted anchored neighborhood regression for fast super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 2014
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Conditioned regression models for non-blind single image super-resolution
Gernot Riegler, Samuel Schulter, Matthias Ruther, and Horst Bischof · 2015
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Revisiting single image super-resolution under internet environment: blur kernels and reconstruction algorithms
Kai Zhang, Xiaoyu Zhou, Hongzhi Zhang, and Wangmeng Zuo · 2015
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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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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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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 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
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Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
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Learning a no-reference quality metric for single-image super-resolution
Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang · 2017
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Waterloo exploration database: New challenges for image quality assessment models
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang · 2017
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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 · 2017
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Benchmarking denoising algorithms with real photographs
Tobias Plotz and Stefan Roth · 2017
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Enhancenet: Single image super-resolution through automated texture synthesis
Mehdi SM Sajjadi, Bernhard Schölkopf, and Michael Hirsch · 2017
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Methods and results
Toward real-world single image super-resolution: A new benchmark and a new model
Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang · 2019
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Blind image super-resolution with spatially variant degradations
Victor Cornillere, Abdelaziz Djelouah, Wang Yifan, Olga Sorkine-Hornung, and Christopher Schroers · 2019
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Frequency separation for real-world super-resolution
Manuel Fritsche, Shuhang Gu, and Radu Timofte · 2019
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Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
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Lightweight image super-resolution with information multi-distillation network
Zheng Hui, Xinbo Gao, Yunchu Yang, and Xiumei Wang · 2019
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A style-based generator architecture for generative adversarial networks
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Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang · 2017
Cited alongside, same era.
Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 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.
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.
“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, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Cited alongside, same era.
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Unsupervised learning for real-world super-resolution
Andreas Lugmayr, Martin Danelljan, and Radu Timofte · 2019
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Deep plug-and-play super-resolution for arbitrary blur kernels
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2019
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Pipal: a large-scale image quality assessment dataset for perceptual image restoration
Jinjin Gu, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Jimmy Ren, and Chao Dong · 2020
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Real-world super-resolution via kernel estimation and noise injection
Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang · 2020
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Ntire 2020 challenge on real-world image super-resolution: Methods and results
Andreas Lugmayr, Martin Danelljan, and Radu Timofte · 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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Aim 2020 challenge on real image super-resolution: Methods and results
Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, et al · 2020
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Deep unfolding network for image super-resolution
Kai Zhang, Luc Van Gool, and Radu Timofte · 2020
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Towards flexible blind JPEG artifacts removal
Jiaxi Jiang, Kai Zhang, and Radu Timofte · 2021
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Hierarchical conditional flow: A unified framework for image super-resolution and image rescaling
Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2021
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Flow-based kernel prior with application to blind super-resolution
Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte · 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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Plug-and-play image restoration with deep denoiser prior
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte · 2021
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