L1-L2 hybrid noise model to image super-resolution

DOI

10.2991/ameii-15.2015.313How to use a DOI?

Keywords

Hybrid noise model; Super-resolution; L1 norm; L2 norm; adaptive membership degree.

Abstract

L1-L2 hybrid noise model (HNM) method is proposed in this paper for image/video super-resolution. This method has the advantages of both L1 norm minimization (i.e. edge preservation) and L2 norm minimization (i.e. smoothing characterization). In view of noise distribution changing and selecting L1 norm minimization or L2 norm minimization, we propose an efficient adaptive membership degree (AMD) method, which get the ideal result but the proposed AMD method can reduce the number of iterations and save much computational cost. Experimental results indicate that the proposed method is of higher peak signal to noise ratio (PSNR) and structural similarity (SSIM). And it has better reconstructed effect in edge and smoothing part.

Copyright

© 2015, 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  - Junkui Li
AU  - Hui Liu
AU  - Zhenhong Shang
PY  - 2015/04
DA  - 2015/04
TI  - L1-L2 hybrid noise model to image super-resolution
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 1682
EP  - 1688
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.313
DO  - 10.2991/ameii-15.2015.313
ID  - Li2015/04
ER  -