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Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones.
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 · 2002
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Supervised dictionary learning
Julien Mairal, Jean Ponce, Guillermo Sapiro, Andrew Zisserman, and Francis Bach · 2008
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Online learning for matrix factorization and sparse coding
Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro · 2010
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Image super-resolution via sparse representation
Jianchao Yang, John Wright, Thomas S Huang, and Yi Ma · 2010
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Single image super-resolution using gaussian process regression
He He and Wan-Chi Siu · 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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On Single Image Scale-Up Using Sparse-Representations , page 711–730
Roman Zeyde, Michael Elad, and Matan Protter · 2012
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Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, 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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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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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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Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, et al · 2017
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Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Fast, accurate, and lightweight super-resolution with cascading residual network, 2018
Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Cited alongside, same era.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
Cited alongside, same era.
Lightweight image super-resolution with information multi-distillation network
Zheng Hui, Xinbo Gao, Yunchu Yang, and Xiumei Wang · 2019
Cited alongside, same era.
Pre-trained image processing transformer, 2020
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2020
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 · 2020
Swinir: Image restoration using swin transformer, 2021
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows, 2021
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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Cross aggregation transformer for image restoration
Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan · 2022
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N-gram in swin transformers for efficient lightweight image super-resolution, 2022
Haram Choi, Jeongmin Lee, and Jihoon Yang · 2022
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Swin transformer v2: Scaling up capacity and resolution
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Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
Cited alongside, same era.
Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond, 2020
Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, and Jiaya Jia · 2020
Cited alongside, same era.
LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block , page 272–289
Xiaotong Luo, Yuan Xie, Yulun Zhang, Yanyun Qu, Cuihua Li, and Yun Fu · 2020
Cited alongside, same era.
Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining
Yiqun Mei, Yuchen Fan, Yuqian Zhou, Lichao Huang, Thomas S Huang, and Humphrey Shi · 2020
Cited alongside, same era.
Single Image Super-Resolution via a Holistic Attention Network , page 191–207
Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, and Haifeng Shen · 2020
Cited alongside, same era.
Twins: Revisiting the design of spatial attention in vision transformers, 2021
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 2021
Cited alongside, same era.
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al · 2022
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2022
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Efficient long-range attention network for image super-resolution
Xindong Zhang, Hui Zeng, Shi Guo, and Lei Zhang · 2022
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Activating more pixels in image super-resolution transformer
Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong · 2023
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Swin2sr: Swinv2 transformer for compressed image super-resolution and restoration
Marcos V Conde, Ui-Jin Choi, Maxime Burchi, and Radu Timofte · 2023
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Efficient and explicit modelling of image hierarchies for image restoration
Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool · 2023
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Omni aggregation networks for lightweight image super-resolution
Hang Wang, Xuanhong Chen, Bingbing Ni, Yutian Liu, and Liu jinfan · 2023
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Accurate image restoration with attention retractable transformer
Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan · 2023
Later among the works it cites.