SAR Speckle Nonlocal Filtering With Statistical Modeling of Haar Wavelet Coefficients and Stochastic Distances

Abstract

Due to the coherent processing of synthetic aperture radar (SAR) systems, multiplicative speckle noise arises providing a granular appearance in SAR images. This kind of noise makes it difficult to analyze and interpret surface images from the earth. Therefore, studying alternatives to attenuate the speckle is a constant task in the image processing literature. Current state-of-the-art filters in remote sensing area explore the philosophy of similarity between patches. This paper aims to expand the traditional nonlocal means (NLM) algorithm originally proposed for the additive white Gaussian noise (AWGN) to deal with the speckle. In our research, we consider the worst scenario, i.e., the single-look speckle noise, and apply the NLM to filter intensity SAR images in the Haar wavelet domain. To accomplish this task, the Haar coefficients were described by exponential-polynomial (EP) and gamma distributions. Furthermore, stochastic distances based on these two mentioned distributions were derived and embedded in the NLM filter by replacing the Euclidean distance of the original method. This represents the main contribution of the proposed research. Finally, this paper analyzes and compares the synthetic and real experiments of the proposed method with some recent filters of the literature demonstrating its competitive performance.