MAP Despeckling of SAR Images Based on Local PDF Modeling in the Undecimated Wavelet Domain
Fabrizio Argenti, Tiziano Bianchi, Luciano Alparone · 2006
In this paper, a new despeckling method based on undec-imated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (PDF) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG PDF are taken to be space-varying within each wavelet frame. The variance and shape fac-tor of the GG function are derived from the theoretical moments, which depend on the moments and joint mo-ments of the observed noisy signal and on the statistics of speckle. Experimental results demonstrate that MAP filtering can be successfully applied to SAR images repre-sented in the shift-invariant wavelet domain. 1.