SAR image denoising using total variation based regularization with sure-based optimization of the regularization parameter

Abstract

Images obtained using Synthetic Aperture Radar (SAR) are corrupted by speckle. Speckle noise results from the chaotic interference of backscattered electromagnetic waves and makes the analysis, interpretation and classification of SAR images difficult. In this paper, we present a denoising algorithm based on Total Variation (TV) regularization. While this kind of denoising algorithm is not new, we propose to select the regularization parameter by minimizing the estimate of the mean square error (MSE) between the denoised image and the clean image. We do not have to know the clean image because we use a statistically unbiased MSE estimate - Stein's Unbiased Risk Estimate (SURE), that depends on the observed image and the estimated image. However, since it is difficult to derive SURE analytically for this kind of problem, we estimate SURE using stochastic methods. We present results using both a simulated image and real SAR image.