SAR image denoising in nonsubsampled contourlet transform domain based on maximum a posteriori and non-local constraint

Abstract

An approach of synthetic aperture radar (SAR) image denoising in nonsubsampled contourlet transform (NSCT) domain based on maximum a posteriori (MAP) and non-local (N-L) constraint is proposed. SAR image is firstly modelled by a nonlogarithmic additive model for modelling of the speckle in NSCT domain. Then, coefficients of real signals are obtained in the NSCT domain with MAP adaptive shrinkage. As it tends to eliminate too many coefficients that contain useful information by shrinkage, the N-L constraint is introduced to smooth the coefficients left in each subband, for each pixel in the subbands of NSCT corresponding to those in the same location of the original image. Experiments show that the proposed approach is effective in SAR image denoising and texture preserving, in comparison with some traditional algorithms.