SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling

Alin Achim, Panagiotis Tsakalides, Anastasios Bezerianos

Research output: Contribution to journalArticlepeer-review

372 Scopus citations


Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.

Original languageEnglish (US)
Pages (from-to)1773-1784
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
StatePublished - Aug 2003
Externally publishedYes


  • Maximum a posteriori (MAP) estimation
  • Symmetric alpha-stable distributions
  • Synthetic aperture radar (SAR) speckle
  • Wavelet decomposition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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