A low-complexity MMSE Bayesian estimator for suppression of speckle in SAR images

In synthetic aperture radar (SAR) images, speckle noise reduction is a crucial pre-processing step for their successful interpretation and thus has drawn a great deal of attention of researchers in the image processing community. The Bayesian estimation is a powerful signal estimation technique and has been widely used for speckle noise removal in images. In this work, a low complexity wavelet-based Bayesian estimation technique for despeckling of images is developed. The main idea of the proposed technique is in establishing suitable statistical models for the wavelet coefficients and then in using these models to develop a shrinkage function with a low-complexity realization for the estimation of the wavelet coefficients of the noise-free images. The experimental results demonstrate the effectiveness of the proposed despeckling scheme in providing a significant reduction in the speckle noise at a very low computational cost and simultaneously preserving the image details.