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Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used in existing super-resolution (SR) methods.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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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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Photon, poisson noise
Samuel W. Hasinoff · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Accelerating the super-resolution convolutional neural network
Chao Dong, Chen Change Loy, and Xiaoou Tang · 2016
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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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Benchmarking denoising algorithms with real photographs
Tobias Plotz and Stefan Roth · 2017
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Jpeg-resistant adversarial images
Richard Shin and Dawn Song · 2017
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Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang · 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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To learn image super-resolution, use a gan to learn how to do image degradation first
Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos · 2018
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Ntire 2018 challenge on single image super-resolution: Methods and results
Radu Timofte, Shuhang Gu, Jiqing Wu, and Luc Van Gool · 2018
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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.
Learning a single convolutional super-resolution network for multiple degradations
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
Cited alongside, same era.
Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
Cited alongside, same era.
Deep generative adversarial residual convolutional networks for real-world super-resolution
Rao Muhammad Umer, Gian Luca Foresti, and Christian Micheloni · 2020
Later among the works it cites.
Deep unfolding network for image super-resolution
Kai Zhang, Luc Van Gool, and Radu Timofte · 2020
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Learning to generate realistic noisy images via pixel-level noise-aware adversarial training
Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, and Donglai Wei · 2021
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Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, and Luc Van Gool · 2021
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Approaching the limit of image rescaling via flow guidance
Shang Li, Guixuan Zhang, Zhengxiong Luo, Jie Liu, Zhi Zeng, and Shuwu Zhang · 2021
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Blind super-resolution kernel estimation using an internal-gan
Sefi Bell-Kligler, Assaf Shocher, and Michal Irani · 2019
Cited alongside, same era.
Unprocessing images for learned raw denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron · 2019
Cited alongside, same era.
Frequency separation for real-world super-resolution
Manuel Fritsche, Shuhang Gu, and Radu Timofte · 2019
Cited alongside, same era.
Blind super-resolution with iterative kernel correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong · 2019
Cited alongside, same era.
Deep plug-and-play super-resolution for arbitrary blur kernels
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2019
Cited alongside, same era.
Unsupervised image super-resolution with an indirect supervised path
Shuaijun Chen, Zhen Han, Enyan Dai, Xu Jia, Ziluan Liu, Liu Xing, Xueyi Zou, Chunjing Xu, Jianzhuang Liu, and Qi Tian · 2020
Cited alongside, same era.
Unsupervised image super-resolution with an indirect supervised path
Shuaijun Chen, Zhen Han, Enyan Dai, Xu Jia, Ziluan Liu, Liu Xing, Xueyi Zou, Chunjing Xu, Jianzhuang Liu, and Qi Tian · 2020
Cited alongside, same era.
Later among the works it cites.
Blind image super-resolution: A survey and beyond
Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, and Chao Dong · 2021
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Efficient super resolution by recursive aggregation
Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, and Tieniu Tan · 2021
Later among the works it cites.
End-to-end alternating optimization for blind super resolution
Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, and Tieniu Tan · 2021
Later among the works it cites.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021
Later among the works it cites.
Unsupervised real-world image super resolution via domain-distance aware training
Yunxuan Wei, Shuhang Gu, Yawei Li, Radu Timofte, Longcun Jin, and Hengjie Song · 2021
Later among the works it cites.
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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Rformer: Transformer-based generative adversarial network for real fundus image restoration on a new clinical benchmark
Zhuo Deng, Yuanhao Cai, Lu Chen, Zheng Gong, Qiqi Bao, Xue Yao, Dong Fang, Shaochong Zhang, and Lan Ma · 2022
Closest in time.
Dfan: Dual feature aggregation network for lightweight image super-resolution
Shang Li, Guixuan Zhang, Zhengxiong Luo, and Jie Liu · 2022
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From general to specific: Online updating for blind super-resolution
Shang Li, Guixuan Zhang, Zhengxiong Luo, Jie Liu, Zhi Zeng, and Shuwu Zhang · 2022
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Flow-guided sparse transformer for video deblurring
Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, and Luc Van Gool · 2022
Closest in time.