Speckle reduction of SAR images using sure-based adaptive Sigmoid thresholding in the wavelet domain

Abstract

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the images and makes interpretation, analysis and classification of SAR images harder. Therefore, some speckle reduction is necessary prior to the processing of SAR images. The speckle noise can be modeled as multiplicative i.i.d. Rayleigh noise. Recently, Sveinsson and Benediktsson [6], proposed an adaptive sigmoid thresholding method for SAR images in the wavelet domain. The coefficients thresholding for this method is based on the choice of parameters in the Sigmoid thresholding function. They were chosen according to a visual appreciation, i.e., by ad hoc method. We propose to select these parameters by minimizing an estimate of square error between the clean image and the denoised one. The key point is that we have in our proposal computable, statistically unbiased, MSE estimate — Stein's Unbiased Risk Estimate (SURE) — that depends on the noisy image alone, not on the clean image. We apply the proposed method on an SAR images, both simulated and real data.