Kalman-S Shrinkage For Wavelet-Based Despeckling Of Sar Images
Mario Mastriani, Alberto Giráldez · Zenodo (CERN European Organization for Nuclear Research) · 2008
In this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman-s filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman-s filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients. Experimental results demonstrate that our method compares favorably to several other despeckling methods on test synthetic aperture radar (SAR) images.