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

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.