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Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images).
Two deterministic half-quadratic regularization algorithms for computed imaging
Pierre Charbonnier, Laure Blanc-Feraud, Gilles Aubert, and Michel Barlaud · 1994
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Kodak lossless true color image suite
Rich Franzen · 1999
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
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Live image quality assessment database release 2
HR Sheikh · 2005
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Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Pointwise shape-adaptive dct for high-quality denoising and deblocking of grayscale and color images
Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Single image haze removal using dark channel prior
Kaiming He, Jian Sun, and Xiaoou Tang · 2010
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On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 2010
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Color demosaicking by local directional interpolation and nonlocal adaptive thresholding
Lei Zhang, Xiaolin Wu, Antoni Buades, and Xin Li · 2011
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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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Fast image super resolution via local regression
Shuhang Gu, Nong Sang, and Fan Ma · 2012
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Nonparametric blind super-resolution
Tomer Michaeli and Michal Irani · 2013
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Anchored neighborhood regression for fast example-based super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 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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Weighted nuclear norm minimization with application to image denoising
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng · 2014
Earlier work this paper cites.
A+: Adjusted anchored neighborhood regression for fast super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 2014
Earlier work this paper cites.
Compression artifacts reduction by a deep convolutional network
Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang · 2015
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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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Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Yunjin Chen and Thomas Pock · 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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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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Cas-cnn: A deep convolutional neural network for image compression artifact suppression
Lukas Cavigelli, Pascal Hager, and Luca Benini · 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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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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Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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Learning deep cnn denoiser prior for image restoration
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang · 2017
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Fast, accurate, and lightweight super-resolution with cascading residual network
Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 2018
Cited alongside, same era.
Deep back-projection networks for super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2018
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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.
Multi-level wavelet-cnn for image restoration
Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, and Wangmeng Zuo · 2018
Cited alongside, same era.
Neural nearest neighbors networks
Tobias Plötz and Stefan Roth · 2018
Cited alongside, same era.
Single image super-resolution via a holistic attention network
Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, and Haifeng Shen · 2020
Later among the works it cites.
Image denoising using deep cnn with batch renormalization
Chunwei Tian, Yong Xu, and Wangmeng Zuo · 2020
Later among the works it cites.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Visual transformers: Token-based image representation and processing for computer vision
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda · 2020
Later among the works it cites.
Identifying recurring patterns with deep neural networks for natural image denoising
Zhihao Xia and Ayan Chakrabarti · 2020
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Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Cited alongside, same era.
Ffdnet: Toward a fast and flexible solution for cnn-based image denoising
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 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.
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.
On the relationship between self-attention and convolutional layers
Jean-Baptiste Cordonnier, Andreas Loukas, and Martin Jaggi · 2019
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
Cited alongside, same era.
Later among the works it cites.
Residual dense network for image restoration
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2020
Later among the works it cites.
Cross-scale internal graph neural network for image super-resolution
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, and Chen Change Loy · 2020
Later among the works it cites.
Swin-unet: Unet-like pure transformer for medical image segmentation
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang · 2021
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Video super-resolution transformer
Jiezhang Cao, Yawei Li, Kai Zhang, and Luc Van Gool · 2021
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Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
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Mfagan: A compression framework for memory-efficient on-device super-resolution gan
Wenlong Cheng, Mingbo Zhao, Zhiling Ye, and Shuhang Gu · 2021
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Deep coupled feedback network for joint exposure fusion and image super-resolution
Xin Deng, Yutong Zhang, Mai Xu, Shuhang Gu, and Yiping Duan · 2021
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A model-driven deep unfolding method for jpeg artifacts removal
Xueyang Fu, Menglu Wang, Xiangyong Cao, Xinghao Ding, and Zheng-Jun Zha · 2021
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Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, and Jiaya Jia · 2021
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Localvit: Bringing locality to vision transformers
Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool · 2021
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Transcrowd: Weakly-supervised crowd counting with transformer
Dingkang Liang, Xiwu Chen, Wei Xu, Yu Zhou, and Xiang Bai · 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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Mutual affine network for spatially variant kernel estimation in blind image super-resolution
Jingyun Liang, Guolei Sun, Kai Zhang, 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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Transformer in convolutional neural networks
Yun Liu, Guolei Sun, Yu Qiu, Le Zhang, Ajad Chhatkuli, and Luc Van Gool · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Image super-resolution with non-local sparse attention
Yiqun Mei, Yuchen Fan, and Yuqian Zhou · 2021
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Boosting crowd counting with transformers
Guolei Sun, Yun Liu, Thomas Probst, Danda Pani Paudel, Nikola Popovic, and Luc Van Gool · 2021
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Scaling local self-attention for parameter efficient visual backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, and Jonathon Shlens · 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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Learning a single network for scale-arbitrary super-resolution
Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo · 2021
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Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021
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Uformer: A general u-shaped transformer for image restoration
Zhendong Wang, Xiaodong Cun, Jianmin Bao, and Jianzhuang Liu · 2021
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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
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Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross Girshick · 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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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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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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