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Proceedings Paper

SAR image denoising based on alpha-stable distribution and Bayesian wavelet shrinkage
Author(s): Xin Xu; Yin Zhao; Wanbin Zhou; Yijin Peng
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Paper Abstract

In this paper, an algorithm for synthetic aperture radar (SAR) image denoising in the wavelet domain is presented. The alpha-stable distribution is applied to model the wavelet coefficients of the logarithmically transformed SAR images and the Gaussian mixture model to represent the Speckle. The method of regression-type is used to estimate the four parameters of the alpha-stable distribution and EM algorithm to estimate the variance of the noise respectively. Since the alpha-stable distribution do not always have a closed-form formula, Zolotarev's (M) parameterization is exploited to obtain the probability density function (PDF) of the alpha-stable distribution. Consequently, a maximum a posteriori (MAP) estimator is designed based on the alpha-stable prior to restore the SAR image. The experimental results, including simulated SAR image and SIR-C/X-band SAR image, indicate that the proposed algorithm has capability both in Speckle suppression and details preservation.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74951U (30 October 2009); doi: 10.1117/12.832917
Show Author Affiliations
Xin Xu, Wuhan Univ. (China)
Yin Zhao, Wuhan Univ. (China)
Wanbin Zhou, Wuhan Univ. (China)
Yijin Peng, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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