Curvelet-Based Bayesian Estimator for Speckle Suppression in Ultrasound Imaging

Ultrasound images are inherently affected by speckle noise, and thus reducing this noise is crucial for successful post-processing. One powerful approach for noise suppression in digital images is Bayesian estimation. In the Bayesian-based despeckling schemes, the choice of suitable statistical models and the development of a shrinkage function for estimation of the noise-free signal are the major concerns. In this paper, a novel curvelet-based Bayesian estimator for speckle removal in ultrasound images is developed. The curvelet coefficients of the degradation model of the noisy ultrasound image are decomposed into two components, namely noise-free signal and signal-dependent noise. The Cauchy and two-sided exponential distributions are assumed to be statistical models for the two components, respectively, and an efficient low-complexity realization of the Bayesian estimator is proposed. The experimental results demonstrate the superiority of the proposed despeckling scheme in achieving significant speckle suppression and preserving image details.