Synthetic Aperture Radar (SAR) image processing plays a vital role in observing the earth and in understanding its varied features. A SAR image contains edges and shapes hidden by speckle noise. Therefore, despeckling is essential for subsequent feature extraction and classification. This paper presents a new despeckling method based on Non-subsampled Contourlet Transform (NSCT) and Bayesian Maximum A Posterior (BMAP) estimation. NSCT effectively captures the SAR image features as multi-scale and multidirectional information. BMAP is a point estimation based on statistical prior distribution. So, BMAP estimation represents the aggregate behavior in each direction of the NSCT neighborhood coefficients using the statistical prior models. The dependency relationship of NSCT neighborhood coefficients by the statistical priors and BMAP of point estimation shrinks the speckle noise coefficients. In this work, the NSCT higher frequency coefficients are de-speckled, since higher frequency coefficients contains more detail and more noise. This despeckling method is compared with the state-of-the-art methods using a set of reference and non-referenced quality metrics. Experimental results show that this developed method is superior to the other methods used for preserving information and for eliminating speckle noise.
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