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SAR Images Despeckling via Bayesian Fuzzy Shrinkage Based on Stationary Wavelet Transform

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Wavelet Analysis and Applications

Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

Abstract

An efficient despeckling method is proposed based on stationary wavelet transform (SWT) for synthetic aperture radar (SAR) images. The statistical model of wavelet coefficients is analyzed and its performance is modeled with a mixture density of two zero-mean Gaussian distributions. A fuzzy shrinkage factor is derived based on the minimum mean error (MMSE) criteria with bayesian estimation. In this case, the ideas of region division and fuzzy shrinkage are adopted according to the interscale dependencies among wavelet coefficients. The noise-free wavelet coefficients are estimated accurately. Experimental results show that our method outperforms the refined Lee filterwavelet soft thresholding shrinkage and SWT shrinkage algorithms in terms of smoothing effects and edges preservation.

This work was supported by the National Defence Foundation (51431020204DZ0101) and the Postdoctoral Science Foundation of China (J63104020156).

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Author information

Authors and Affiliations

  1. National key lab. of radar signal processing, Xidian Univ., Xi’an, 710071, China

    Yan Wu & Guisheng Liao

  2. School of electronics engineering, Xidian Univ., Xi’an, 710071, China

    Yan Wu & Xia Wang

Authors
  1. Yan Wu
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  2. Xia Wang
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  3. Guisheng Liao
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Editor information

Editors and Affiliations

  1. Faculty of Science and Technology, University of Macau, P.O. Box 3001, Macau

    Tao Qian  & Mang I Vai  & 

  2. Department of Mathematics, Syracuse University, 215 Carnegie Hall, Syracuse, NY, 13244-1150, USA

    Yuesheng Xu

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© 2006 Birkhäuser Verlag Basel/Switzerland

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Wu, Y., Wang, X., Liao, G. (2006). SAR Images Despeckling via Bayesian Fuzzy Shrinkage Based on Stationary Wavelet Transform. In: Qian, T., Vai, M.I., Xu, Y. (eds) Wavelet Analysis and Applications. Applied and Numerical Harmonic Analysis. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7778-6_29

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