Year: 2022
Author: Xiao-Guang Lv, Fang Li, Jun Liu, Sheng-Tai Lu
Advances in Applied Mathematics and Mechanics, Vol. 14 (2022), Iss. 1 : pp. 155–180
Abstract
Ultrasound is a low-cost, non-invasive and real-time imaging modality that has proved popular for many medical applications. Unfortunately, the acquired ultrasound images are often corrupted by speckle noise from scatterers smaller than ultrasound beam wavelength. The signal-dependent speckle noise makes visual observation difficult. In this paper, we propose a patch-based low-rank approach for reducing the speckle noise in ultrasound images. After constructing the patch group of the ultrasound images by the block-matching scheme, we establish a variational model using the weighted nuclear norm as a regularizer for the patch group. The alternating direction method of multipliers (ADMM) is applied for solving the established nonconvex model. We return all the approximate patches to their original locations and get the final restored ultrasound images. Experimental results are given to demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and quantitative measures.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/aamm.OA-2021-0011
Advances in Applied Mathematics and Mechanics, Vol. 14 (2022), Iss. 1 : pp. 155–180
Published online: 2022-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 26
Keywords: Ultrasound images patch speckle noise low-rank weighted nuclear norm minimization.