Fast Patchwise Nonlocal SAR Image Despeckling Using Joint Intensity and Structure Measures

Abstract

Nonlocal means synthetic aperture radar (SAR) image despeckling approaches have attracted much research attention. However, high computational burden always limits its application in practice, especially using complex similarity measures. We present a fast patchwise nonlocal method using joint intensity and structure measures for SAR image despeckling. Nonlocal methods often define the similarity criterion only based on amplitude or intensity image. In order to preserve structure details, the structure information is also introduced into similarity measure by constructing gradient orientation feature map. The gradient orientation statistical test is performed to determine whether the patches contain the same structural components, and the similar patches are selected through the constant false alarm ratio strategy. Furthermore, we reorganize the patchwise nonlocal despeckling method and accelerate it using fast Fourier transform. Meanwhile, we utilize a Gaussian kernel to aggregate patchwise weights for each pixel in its patch area, so as to reduce the blur effect of classical patchwise nonlocal methods on details. The experiments have demonstrated that the proposed method is an efficient restoration method and has great structure and texture retention ability.