Denoising Method Based on the Nonsubsampled Shearlet Transform

DOI

10.2991/iiicec-15.2015.56How to use a DOI?

Keywords

nonsubsampled Shearlet; Bivariate Shrinkage; image denoising

Abstract

In this paper, a new bivariate shrinkage denoising method is proposed to model statistics of shearlet coefficients of images. Using Bayesian estimation theory we derive from this model a simple non-linear shrinkage function for shearlet denoising, which generalizes the soft threshold approach. Experimental results show that the proposed method can remove Gaussian white noise while effectively preserving edges and texture information. At the same time, it can achieve a higher PSNR and mean structural similarity than other denoising method.

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  - Xiaoxia Ren
AU  - Zuoyu Wei
AU  - Zhifang He
AU  - Xiuming Sun
AU  - Peng Geng
PY  - 2015/03
DA  - 2015/03
TI  - Denoising Method Based on the Nonsubsampled Shearlet Transform
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 237
EP  - 240
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.56
DO  - 10.2991/iiicec-15.2015.56
ID  - Ren2015/03
ER  -