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Video super-resolution plays an important role in surveillance video analysis and ultra-high-definition video display, which has drawn much attention in both the research and industrial communities.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
On bayesian adaptive video super resolution
Ce Liu and Deqing Sun · 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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Bidirectional recurrent convolutional networks for multi-frame super-resolution
Yan Huang, Wei Wang, and Liang Wang · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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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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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 Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi · 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
Cited alongside, same era.
Image super-resolution via deep recursive residual network
Ying Tai, Jian Yang, and Xiaoming Liu · 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.
Re3: Re al-time recurrent regression networks for visual tracking of generic objects
Daniel Gordon, Ali Farhadi, and Dieter Fox · 2018
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
Tdan: Temporally deformable alignment network for video super-resolution
Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu · 2018
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Temporal deformable convolutional encoder-decoder networks for video captioning
Jingwen Chen, Yingwei Pan, Yehao Li, Ting Yao, Hongyang Chao, and Tao Mei · 2019
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Efficient video super-resolution through recurrent latent space propagation
Dario Fuoli, Shuhang Gu, and Radu Timofte · 2019
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Recurrent back-projection network for video super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2019
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Edvr: Video restoration with enhanced deformable convolutional networks
Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy · 2019
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Cited alongside, same era.
3dsrnet: Video super-resolution using 3d convolutional neural networks
Soo Ye Kim, Jeongyeon Lim, Taeyoung Na, and Munchurl Kim · 2018
Cited alongside, same era.
Frame-recurrent video super-resolution
Mehdi SM Sajjadi, Raviteja Vemulapalli, and Matthew Brown · 2018
Cited alongside, same era.
Video super-resolution with recurrent structure-detail network
Takashi Isobe, Xu Jia, Shuhang Gu, Songjiang Li, Shengjin Wang, and Qi Tian
Cited in the paper.
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
Cited in the paper.
Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee
Cited in the paper.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee
Cited in the paper.
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.
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.
Eleatt-rnn: Adding attentiveness to neurons in recurrent neural networks
Pengfei Zhang, Jianru Xue, Cuiling Lan, Wenjun Zeng, Zhanning Gao, and Nanning Zheng · 2019
Later among the works it cites.