A novel iterative non-local means algorithm for speckle reduction

Abstract

Despeckling of ultrasound images is a crucial step for facilitating subsequent image processing. The non-local means (NLM) filter has been widely applied for denoising images corrupted by Gaussian noise. However, the direct application of this filter in ultrasound images cannot provide satisfactory restoration results. To address this problem, a novel iterative adaptive non-local means (IANLM) filter is proposed to despeckle ultrasound images. In the proposed filter, the speckle noise is firstly transformed into additive Gaussian noise by square root operation. Then the decay parameter is estimated based on a selected homogeneous region. Finally, an iterative strategy combined with the local clustering method based on pixel intensities is adopted to realize effective image smoothing while preserving image edges. Comparisons of the restoration performance of IANLM filter with other state-of-the-art despeckling methods are made. The quantitative comparisons of despeckling synthetic images based on Peak signal-to-noise ratio (PSNR) show that the IANLM filter can provide the best restoration performance among all the evaluated filters. The subjective visual comparisons of the denoised synthetic and ultrasound images demonstrate that the IANLM filter outperforms other compared algorithms in that it can achieve better performance of noise reduction, artifact avoidance, edges and textures preservation and contrast enhancement.