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Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Convolutional lstm network: a machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Wang Chun Woo, Wang Chun Woo, and Wang Chun Woo · 2015
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Image super-resolution using deep convolutional networks
Chao Dong, Change Loy Chen, Kaiming He, and Xiaoou Tang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Video super-resolution with convolutional neural networks
Armin Kappeler, Seunghwan Yoo, Qiqin Dai, and Aggelos K. Katsaggelos · 2016
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Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Real-time video super-resolution with spatio-temporal networks and motion compensation
Jose Caballero, Christian Ledig, Andrew Peter Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2017
Earlier work this paper cites.
Deep laplacian pyramid networks for fast and accurate super-resolution
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang · 2017
Earlier work this paper cites.
Video superresolution via motion compensation and deep residual learning
Dingyi Li and Zengfu Wang · 2017
Earlier work this paper cites.
Robust video super-resolution with learned temporal dynamics
Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, and Thomas Huang · 2017
Cited alongside, same era.
Detail-revealing deep video super-resolution
Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, and Jiaya Jia · 2017
Cited alongside, same era.
Deep back-projection networks for super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2018
Cited alongside, same era.
Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation
Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim · 2018
Cited alongside, same era.
Frame-recurrent video super-resolution
Mehdi S. M Sajjadi, Raviteja Vemulapalli, and Matthew Brown · 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
Edvr: Video restoration with enhanced deformable convolutional networks
Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, and Chen Change Loy · 2019
Later among the works it cites.
Multi-memory convolutional neural network for video super-resolution
Zhongyuan Wang, Peng Yi, Kui Jiang, Junjun Jiang, Zhen Han, Tao Lu, and Jiayi Ma · 2019
Later among the works it cites.
Video enhancement with task-oriented flow
Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman · 2019
Later among the works it cites.
Frame and feature-context video super-resolution
Bo Yan, Chuming Lin, and Weimin Tan · 2019
Later among the works it cites.
Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations
Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma · 2019
Later among the works it cites.
Video super-resolution with recurrent structure-detail network
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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.
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.
Efficient video super-resolution through recurrent latent space propagation
Dario Fuoli, Shuhang Gu, and Radu Timofte · 2019
Cited alongside, same era.
Recurrent back-projection network for video super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2019
Cited alongside, same era.
Takashi Isobe, Xu Jia, Shuhang Gu, Songjiang Li, Shengjin Wang, and Qi Tian · 2020
Later among the works it cites.
Video super-resolution with temporal group attention
Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, and Qi Tian · 2020
Later among the works it cites.
Tdan: Temporally-deformable alignment network for video super-resolution
Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu · 2020
Later among the works it cites.
A progressive fusion generative adversarial network for realistic and consistent video super-resolution
Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, Tao Lu, and Jiayi Ma · 2020
Later among the works it cites.
Multi-temporal ultra dense memory network for video super-resolution
Peng Yi, Zhongyuan Wang, Kui Jiang, Zhenfeng Shao, and Jiayi Ma · 2020
Later among the works it cites.