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Recently, Transformer-based methods have achieved impressive results in single image super-resolution (SISR).
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 kernel statistical test of independence
A. Gretton, K. Fukumizu, C. H. Teo, S. Le, and A. J. Smola · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Low-complexity single image super-resolution based on nonnegative neighbor embedding
M. Bevilacqua, A. Roumy, C. Guillemot, and A. Morel · 2012
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On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep networks for image super-resolution with sparse prior
Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang · 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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Sgdr: Stochastic gradient descent with restarts
I. Loshchilov and F. Hutter · 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.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
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
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.
Sketch-based manga retrieval using manga109 dataset
Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2017
Thao Nguyen, Maithra Raghu, and Simon Kornblith · 2020
Later among the works it cites.
Attention in attention network for image super-resolution
Haoyu Chen, Jinjin Gu, and Zhi Zhang · 2021
Later among the works it cites.
Demystifying local vision transformer: Sparse connectivity, weight sharing, and dynamic weight, 2021
Q. Han, Z. Fan, Q. Dai, L. Sun, and J. Wang · 2021
Later among the works it cites.
Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
Later among the works it cites.
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
Later among the works it cites.
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Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Shift: A zero flop, zero parameter alternative to spatial convolutions
B. Wu, A. Wan, X. Yue, P. Jin, S. Zhao, N. Golmant, A. Gholaminejad, J. Gonzalez, and K. Keutzer · 2017
Cited alongside, same era.
Deep back-projection networks for super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2018
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond
Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, and Jiaya Jia · 2020
Cited alongside, same era.
Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
Later among the works it cites.
N-gram in swin transformers for efficient lightweight image super-resolution
Haram Choi, Jeongmin Lee, and Jihoon Yang · 2022
Later among the works it cites.
Transformer for single image super-resolution
Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng · 2022
Later among the works it cites.
Metaformer is actually what you need for vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, and Shuicheng Yan · 2022
Later among the works it cites.
Efficient long-range attention network for image super-resolution
Xindong Zhang, Hui Zeng, Shi Guo, and Lei Zhang · 2022
Later among the works it cites.
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
Closest in time.
Image super-resolution using efficient striped window transformer
Jinpeng Shi, Hui Li, Tianle Liu, Yulong Liu, Mingjian Zhang, Jinchen Zhu, Ling Zheng, and Shizhuang Weng · 2023
Closest in time.