Synthetic aperture radar image despeckling via spatially adaptive shrinkage in the nonsubsampled contourlet transform domain

Abstract

A new spatially adaptive shrinkage approach based on the nonsubsampled contourlet transform (NSCT) to despeckling synthetic aperture radar (SAR) images is proposed. This method starts from the existing stationary wavelet transform (SWT)–domain Gamma-exponential likelihood model combined with a local spatial prior model and extends the model further for despeckling an SAR image via spatially adaptive shrinkage in the NCST domain. The proposed NSCT-domain shrinkage estimator consists of a new likelihood ratio function and a new prior ratio function, both of which are dependent on the estimated masks for the NSCT coefficients. The former is established by the Gamma distribution with variable scale and shape parameters and the exponential distribution with variable scale parameter to adapt the shrinkage estimator to the redundancy property of the NSCT. Parameters of these two distributions are estimated by using moment-based estimators. The latter is equipped with directional neighborhood configurations to accommodate the estimator to the flexible directionality of the NSCT, and thus to enhance the detail fidelity. We validate the proposed method on real SAR images and demonstrate the excellent despeckling performance through comparisons with the SWT-based counterpart, two classical spatial filters, and the contourlet transform-based despeckling technique.