IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v372y2020ics0096300319309634.html
   My bibliography  Save this article

Cauchy noise removal using group-based low-rank prior

Author

Listed:
  • Ding, Meng
  • Huang, Ting-Zhu
  • Ma, Tian-Hui
  • Zhao, Xi-Le
  • Yang, Jing-Hua

Abstract

Although the extensive research on Gaussian noise removal, few works consider the Cauchy noise removal problem. In this paper, we propose a novel group-based low-rank method for Cauchy noise removal. By exploiting the nonlocal self-similarity of natural images, we consider a group of similar patches as an approximate low-rank matrix, and formulate the denoising of each group as a low-rank matrix recovery problem. Meanwhile, we develop the alternating direction method of multipliers algorithm to solve the proposed nonconvex model with guaranteed convergence. Experiments illustrate that our method has superior performance over the state-of-the-art methods in terms of both visual and quantitative measures.

Suggested Citation

  • Ding, Meng & Huang, Ting-Zhu & Ma, Tian-Hui & Zhao, Xi-Le & Yang, Jing-Hua, 2020. "Cauchy noise removal using group-based low-rank prior," Applied Mathematics and Computation, Elsevier, vol. 372(C).
  • Handle: RePEc:eee:apmaco:v:372:y:2020:i:c:s0096300319309634
    DOI: 10.1016/j.amc.2019.124971
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300319309634
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2019.124971?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Jing-Hua & Zhao, Xi-Le & Ji, Teng-Yu & Ma, Tian-Hui & Huang, Ting-Zhu, 2020. "Low-rank tensor train for tensor robust principal component analysis," Applied Mathematics and Computation, Elsevier, vol. 367(C).
    2. Ding, Meng & Huang, Ting-Zhu & Wang, Si & Mei, Jin-Jin & Zhao, Xi-Le, 2019. "Total variation with overlapping group sparsity for deblurring images under Cauchy noise," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 128-147.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Jingjing & Ma, Ruijie & Zeng, Xiaoyang & Liu, Wanquan & Wang, Mingyu & Chen, Hui, 2021. "An efficient non-convex total variation approach for image deblurring and denoising," Applied Mathematics and Computation, Elsevier, vol. 397(C).
    2. Wu, Tingting & Huang, Chaoyan & Jia, Shilong & Li, Wei & Chan, Raymond & Zeng, Tieyong & Kevin Zhou, S., 2024. "Medical image reconstruction with multi-level deep learning denoiser and tight frame regularization," Applied Mathematics and Computation, Elsevier, vol. 477(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yugang & Huang, Ting-Zhu & Zhao, Xi-Le & Deng, Liang-Jian & Ji, Teng-Yu, 2020. "A convex single image dehazing model via sparse dark channel prior," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    2. Jameel Ahmed Bhutto & Asad Khan & Ziaur Rahman, 2023. "Image Restoration with Fractional-Order Total Variation Regularization and Group Sparsity," Mathematics, MDPI, vol. 11(15), pages 1-23, July.
    3. Yang, Jing-Hua & Zhao, Xi-Le & Ji, Teng-Yu & Ma, Tian-Hui & Huang, Ting-Zhu, 2020. "Low-rank tensor train for tensor robust principal component analysis," Applied Mathematics and Computation, Elsevier, vol. 367(C).
    4. Liu, Jingjing & Ma, Ruijie & Zeng, Xiaoyang & Liu, Wanquan & Wang, Mingyu & Chen, Hui, 2021. "An efficient non-convex total variation approach for image deblurring and denoising," Applied Mathematics and Computation, Elsevier, vol. 397(C).
    5. Karim Ennouri & Slim Smaoui & Mohamed Ali Triki, 2021. "Detection of Urban and Environmental Changes via Remote Sensing," Circular Economy and Sustainability, Springer, vol. 1(4), pages 1423-1437, December.
    6. Keren Li & Sergey Utyuzhnikov, 2023. "Tensor Train-Based Higher-Order Dynamic Mode Decomposition for Dynamical Systems," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    7. Wu, Tingting & Ng, Michael K. & Zhao, Xi-Le, 2021. "Sparsity reconstruction using nonconvex TGpV-shearlet regularization and constrained projection," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    8. Fanhua Shang & Yuanyuan Liu & Fanjie Shang & Hongying Liu & Lin Kong & Licheng Jiao, 2020. "A Unified Scalable Equivalent Formulation for Schatten Quasi-Norms," Mathematics, MDPI, vol. 8(8), pages 1-19, August.
    9. Kuan Li & Chun Huang & Ziyang Yuan, 2021. "Error Estimations for Total Variation Type Regularization," Mathematics, MDPI, vol. 9(12), pages 1-14, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:372:y:2020:i:c:s0096300319309634. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.