Nonlocal means method using weight refining for despeckling of ultrasound images

• An 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.

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.