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

Achim A., Tsakalides P., Bezerianos A.

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

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 non-linear operation on the data and we relate this non-linearity 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.

Source: IEEE transactions on geoscience and remote sensing 41 (2003): 1773–1784.

Publisher: Institute of Electrical and Electronics Engineers,, New York, N.Y. , Stati Uniti d'America