An Affinity-Based Algorithm in Nonsubsampled Contourlet Transform Domain: Application to Synthetic Aperture Radar Image Denoising

Abstract

An algorithm in which affinity is merged into nonsubsampled contourlet transform (NSCT) to denoise synthetic aperture radar (SAR) image is proposed. The important information (boundaries or details) or non-important information (homogeneous area) in a SAR image is estimated based on affinity matrix, by which the affinity-based denoising assigns a high affinity element to the coefficients of NSCT that belong to same region. The foreground and background in NSCT domain are automatically initialized which avoids the need for user initialization. Foreground probability obtained by optimizing objective function can be used to achieve the posterior ratio. Combining the posterior ratio and prior ratio, we can obtain the shrunk coefficients. The proposed algorithm was applied to real SAR images denoising and compared through the SAR image vision effect, the equivalent number of looks (ENL) and the edge sustain index (ESI). Experimental results show that the proposed algorithm outperforms the compared algorithms and achieves the better denoising result and edge preservation.