A Patch-Based Low-Rank Minimization Approach for Speckle Noise Reduction in Ultrasound Images

A Patch-Based Low-Rank Minimization Approach for Speckle Noise Reduction in Ultrasound Images

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.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

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.

Author Details

Xiao-Guang Lv

Fang Li

Jun Liu

Sheng-Tai Lu

  1. Synthetic aperture radar image and its despeckling using variational methods: A Review of recent trends

    Baraha, Satyakam | Sahoo, Ajit Kumar

    Signal Processing, Vol. 212 (2023), Iss. P.109156

    https://doi.org/10.1016/j.sigpro.2023.109156 [Citations: 3]
  2. Non-local adaptive hysteresis despeckling approach for medical ultrasound images

    Rajabi, Mahsa | Golshan, Hanif | Hasanzadeh, Reza P.R.

    Biomedical Signal Processing and Control, Vol. 85 (2023), Iss. P.105042

    https://doi.org/10.1016/j.bspc.2023.105042 [Citations: 7]
  3. Non-Convex High-Order TV and ℓ 0-Norm Wavelet Frame-Based Speckle Noise Reduction

    Liu, Xinwu | Lian, Wenhui

    IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69 (2022), Iss. 12 P.5174

    https://doi.org/10.1109/TCSII.2022.3197237 [Citations: 0]
  4. Nonlocal Matrix Rank Minimization Method for Multiplicative Noise Removal

    Yan, Hui-Yin

    Communications on Applied Mathematics and Computation, Vol. (2024), Iss.

    https://doi.org/10.1007/s42967-024-00396-9 [Citations: 0]
  5. Despeckling of Medical Ultrasound Images Using Kriging Interpolation

    Parvez, Tanzil | Halder, Kalyan Kumar

    2023 6th International Conference on Electrical Information and Communication Technology (EICT), (2023), P.1

    https://doi.org/10.1109/EICT61409.2023.10427880 [Citations: 0]
  6. An Efficient Inexact Gauss–Seidel-Based Algorithm for Image Restoration with Mixed Noise

    Wu, Tingting | Min, Yue | Huang, Chaoyan | Li, Zhi | Wu, Zhongming | Zeng, Tieyong

    Journal of Scientific Computing, Vol. 99 (2024), Iss. 2

    https://doi.org/10.1007/s10915-024-02510-8 [Citations: 0]