Image Super-resolution Reconstruction based on Deep Learning and Sparse Representation
- https://doi.org/10.2991/cnct-16.2017.75How to use a DOI?
- Super-resolution, Deep learning, Denoising auto-encoders, Joint dictionary learning, Sparse Representation
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/cnct-16.2017.75 ID - LEI2016/12 ER -