Image Super-resolution Reconstruction based on Deep Learning and Sparse Representation

DOI

10.2991/cnct-16.2017.75How to use a DOI?

Keywords

Super-resolution, Deep learning, Denoising auto-encoders, Joint dictionary learning, Sparse Representation

Abstract

This paper addresses the problem of super-resolution(SR) image reconstruction based on sparse representation and deep learning. we approached this problem from the dictionary learning. Firstly, in order to realize the correspondence between the sparse representation coefficients, we proposed the method of joint dictionary learning based on Sparse Denoising Auto-Encoders(NSDAE). Secondly, at the stage of reconstruction, in order to achieve high frequency compensation, we proposed the algorithm of iterative error back projection. Finally, experimental results show that the recovered high-resolution image is competitive in quality to images produced by other SR methods.

Copyright

© 2017, 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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TY  - CONF
AU  - Qian LEI
AU  - Zhao-hui ZHANG
AU  - Cun-ming HAO
PY  - 2016/12
DA  - 2016/12
TI  - Image Super-resolution Reconstruction based on Deep Learning and Sparse Representation
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 546
EP  - 555
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.75
DO  - 10.2991/cnct-16.2017.75
ID  - LEI2016/12
ER  -