Image Despeckling Based on Improved LMMSE Wavelet Shrinkage

DOI

10.2991/meic-14.2014.325How to use a DOI?

Keywords

synthetic aperture radar (SAR); speckle;wavelet transform;linear minimum mean square error (LMMSE); kernel regression

Abstract

The automatic interpretation of SAR images is often extremely difficult due to speckle, a signal dependent noise, which is inherent of all active coherent imaging systems. Thus, despeckling has become a crucially important issue in SAR image processing. Wavelet theory provides a powerful tool for detecting image feature at different scales. Wavelet-based algorithms have been widely used to reduce speckle noise. In this paper, an adaptive despeckling method for synthetic aperture radar (SAR) images is proposed based on wavelet shrinkage. It follows the framework of the linear minimum mean square error (LMMSE) filter in the wavelet domain proposed for speckle suppression, but improves the parameter estimation method by taking into account the distribution property of wavelet coefficients based on the bilateral kernel regression. An improved adaptive shrinkage function is obtained and each coefficient is decided separately. Simulation results for the simulated SAR images demonstrate the proposed modified method outperforms some representative SAR despeckling methods when the noise is not serious.

Copyright

© 2014, 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  - Yanqiu Cui
AU  - Min Han
PY  - 2014/11
DA  - 2014/11
TI  - Image Despeckling Based on Improved LMMSE Wavelet Shrinkage
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 1442
EP  - 1446
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.325
DO  - 10.2991/meic-14.2014.325
ID  - Cui2014/11
ER  -