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Convolutional neural networks have allowed remarkable advances in single image super-resolution (SISR) over the last decade.
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
Earlier work this paper cites.
Image super-resolution via sparse representation
Jianchao Yang, John Wright, Thomas S Huang, and Yi Ma · 2010
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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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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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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Accelerating the super-resolution convolutional neural network
Chao Dong, Chen Change Loy, and Xiaoou Tang · 2016
Earlier work this paper cites.
Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Earlier work this paper cites.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Earlier work this paper cites.
Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning
Long Chen, Hanwang Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, and Tat-Seng Chua · 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
Earlier work this paper cites.
Image super-resolution via deep recursive residual network
Ying Tai, Jian Yang, and Xiaoming Liu · 2017
Earlier work this paper cites.
Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
Cited alongside, same era.
Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
Cited alongside, same era.
Fast, accurate, and lightweight super-resolution with cascading residual network
Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Ram: Residual attention module for single image super-resolution
Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon, and Jong-Seok Lee · 2018
Cited alongside, same era.
Non-local recurrent network for image restoration
Channel-wise and spatial feature modulation network for single image super-resolution
Yanting Hu, Jie Li, Yuanfei Huang, and Xinbo Gao · 2019
Later among the works it cites.
Lightweight image super-resolution with information multi-distillation network
Zheng Hui, Xinbo Gao, Yunchu Yang, and Xiumei Wang · 2019
Later among the works it cites.
Lightweight image super-resolution with adaptive weighted learning network
Chaofeng Wang, Zheng Li, and Jun Shi · 2019
Later among the works it cites.
Residual non-local attention networks for image restoration
Y Zhang, K Li, K Li, B Zhong, and Y Fu · 2019
Later among the works it cites.
Hierarchical residual attention network for single image super-resolution
Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Carles Fernández Tena, and Jordi Gonzalez · 2020
Later among the works it cites.
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Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, and Thomas S Huang · 2018
Cited alongside, same era.
Bam: Bottleneck attention module
Jongchan Park, Sanghyun Woo, Joon-Young Lee, and In So Kweon · 2018
Cited alongside, same era.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 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.
Cbam: Convolutional block attention module
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 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.
Dynamic convolution: Attention over convolution kernels
Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu · 2020
Later among the works it cites.
Madnet: A fast and lightweight network for single-image super resolution
Rushi Lan, Long Sun, Zhenbing Liu, Huimin Lu, Cheng Pang, and Xiaonan Luo · 2020
Later among the works it cites.
Residual feature aggregation network for image super-resolution
Jie Liu, Wenjie Zhang, Yuting Tang, Jie Tang, and Gangshan Wu · 2020
Later among the works it cites.
Accurate and efficient single image super-resolution with matrix channel attention network
Hailong Ma, Xiangxiang Chu, and Bo Zhang · 2020
Later among the works it cites.
Lightweight single-image super-resolution network with attentive auxiliary feature learning
Xuehui Wang, Qing Wang, Yuzhi Zhao, Junchi Yan, Lei Fan, and Long Chen · 2020
Later among the works it cites.
Interpreting super-resolution networks with local attribution maps
Jinjin Gu and Chao Dong · 2021
Closest in time.
Cascading and enhanced residual networks for accurate single-image super-resolution
Rushi Lan, Long Sun, Zhenbing Liu, Huimin Lu, Zhixun Su, Cheng Pang, and Xiaonan Luo · 2021
Closest in time.
Efficient image super-resolution using pixel attention
Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, and Chao Dong · 2021
Closest in time.