We propose two methods for speckle suppression of synthetic aperture radar (SAR) images. The first method is based on Bayesian shrinkage and is a thresholding technique. The main problem of applying Bayesian shrinkage in a transformed domain, such as contourlet transform (CT), is finding the optimum threshold value. According to our experimental results, contourlet coefficients are affected by noise differently. It means that some contourlet coefficients belong to the specific sub-bands that are more robust against noise. We use this newfound property to determine the optimum threshold value and to develop our proposed method, which is named the weighted Bayesian shrinkage in contourlet domain. The second method, named the NSCT-, is a model-based approach using a two-sided generalized Gamma distribution () to model the statistics of nonsubsampled contourlet transform (NSCT) coefficients. We use the Bayesian maximum a posteriori estimator to find NSCT despeckled coefficients. Experimental results carried out on both artificially speckled images and the true SAR images show that our two proposed methods outperform other approaches via two point of views, speckle noise reduction and image quality preservation.