SAR image denoising based on alpha-stable distribution and Bayesian wavelet shrinkage

Xin Xu; Yin Zhao; Wanbin Zhou; Yijin Peng

doi:10.1117/12.832917

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30 October 2009 SAR image denoising based on alpha-stable distribution and Bayesian wavelet shrinkage

Xin Xu, Yin Zhao, Wanbin Zhou, Yijin Peng

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Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74951U (2009) https://doi.org/10.1117/12.832917
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

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Xin Xu, Yin Zhao, Wanbin Zhou, and Yijin Peng "SAR image denoising based on alpha-stable distribution and Bayesian wavelet shrinkage", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74951U (30 October 2009); https://doi.org/10.1117/12.832917

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