Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images

Synthetic aperture radar (SAR) images are inherently degraded by multiplicative speckle noise where thresholding-based methods in the transform domain are appropriate. Being sparse, the coefficients in the transformed domain play a key role in the performance of any thresholding methods. It has been shown that the coefficients of nonsubsampled shearlet transform (NSST) are sparser than those of stationary wavelet transform (SWT) for either clean or noisy images. Therefore, it is expected that thresholding-based methods in NSST outperform those in the SWT domain. In this paper, BayesShrink, BiShrink, weighted BayesShrink, and weighted BiShrink in NSST and SWT domains are compared in terms of subjective and objective image assessment. As BayesShrink try to find the optimum threshold for every subband, BiShrink uses coefficients, name parent, to clean up coefficients called child, and the weighted methods consider the coefficients' noise efficiency, which implies that subbands in the transform domain may be affected by noise differently. Two models for considering the parent in the NSST domain are proposed. In addition, for both BayesShrink and BiShrink, considering the weighting factor (coefficients noise efficiency) would improve the performance of the corresponding methods as well. Experimental results show that the weighted-BiShrink despeckling approach in the NSST domain gives an outstanding performance when tested with both artificially speckled images and real SAR images.