Nonlocal means method using weight refining for despeckling of ultrasound images

Speckle noise is an inherent nature of ultrasound images, which have negative effect on image interpretation and diagnostic tasks. In this paper, a nonlocal means method using weight refining for ultrasonic speckle reduction is proposed. Based on a signal-dependent speckle model, a novel similarity weight is derived by Bayesian framework. The weight is iteratively refined in a lower dimensional subspace using principal components analysis (PCA) to improve accuracy of weight and reduce its computational complexity. The weight refining is automatically terminated using mean absolute error based on a fully formed speckle region estimated by a PCA-based method. Simulations on various images demonstrate that our method can provide significant improvement over other evaluated methods. Thus, our method has great potential applications to medical ultrasound imaging. HighlightsAn extension of nonlocal means method to ultrasonic speckle reduction.Weight refining scheme in a lower dimensional PCA subspace.Automatic termination of weight refining scheme using mean absolute error based on an estimated fully formed speckle region.Superior restoration performance compared with existing ultrasound image despeckling methods.Great potential applications to medical ultrasound imaging.