Deep Feature Fusion Network for Compressed Video Super-Resolution-Reference-Cited by-同舟云学术

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Deep Feature Fusion Network for Compressed Video Super-Resolution

Published:2022-04-28 Issue:5 Volume:54 Page:4427-4441
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Wang Yue,Wu Xiaohong,He Xiaohai^ORCID,Ren Chao,Zhang Tingrong

Funder

national natural science foundation of china

fundamental research funds for the central universities

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-022-10816-7.pdf

Reference51 articles.

1. Anoop V, Bipin PR (2020) Super-resolution based automatic diagnosis of retinal disease detection for clinical applications. Neural Process Lett 52(2):1155–1170

2. Brandi F, de Queiroz R, Mukherjee D (2008) Super-resolution of video using key frames and motion estimation. In: 2008 15th IEEE international conference on image processing. IEEE, pp 321–324

3. Cao H, Liu X, Wang Y, Li Y, Lei W (2020) Enhanced down/up-sampling-based video coding using the residual compensation. In: 2020 5th international conference on computer and communication systems (ICCCS). IEEE, pp 286–290

4. Chang B, Meng L, Haber E, Ruthotto L, Begert D, Holtham E (2018) Reversible architectures for arbitrarily deep residual neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

5. Dai Y, Liu D, Wu F (2017) A convolutional neural network approach for post-processing in hevc intra coding. In: International conference on multimedia modeling. Springer, pp 28–39

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1. Design and Implementation of Video Surveillance Quality Inspection System;2023 International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT);2023-09-22

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