Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

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Image Super-Resolution Algorithm Based on Deep Residual Network

Authors
Yang Liu
Corresponding Author
Yang Liu
Available Online May 2018.
DOI
10.2991/ncce-18.2018.161How to use a DOI?
Keywords
super-resolution; resent; convolution neural network.
Abstract

This paper puts forward to the image super-resolution algorithm based on the resent. The algorithm makes use of the depth residual network to reconstruct the low rate image, and the super-resolution images obtained by the cycle are, judged by the convolution neural network. The residual learning is introduced to reduce the complexity and degradation of deep network.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Series
Advances in Intelligent Systems Research
Publication Date
May 2018
ISBN
978-94-6252-517-7
ISSN
1951-6851
DOI
10.2991/ncce-18.2018.161How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - CONF
AU  - Yang Liu
PY  - 2018/05
DA  - 2018/05
TI  - Image Super-Resolution Algorithm Based on Deep Residual Network
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 959
EP  - 962
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.161
DO  - 10.2991/ncce-18.2018.161
ID  - Liu2018/05
ER  -