IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v427y2022ics0096300322002521.html
   My bibliography  Save this article

A fractional variational image denoising model with two-component regularization terms

Author

Listed:
  • Li, Xiao
  • Meng, Xiaoying
  • Xiong, Bo

Abstract

Image denoising is to recover true image from noisy image. Many image deonising models are proposed during the last decades. Some models preserve the margin of tissue, i.e., TV model, while the others, i.e., LLT model, prefer smooth solutions. By decomposing true image into cartoon part and texture part, we propose a fractional image denoising model with two-component regularization terms. Setting some appropriate parameters, the proposed model can deal with both smooth and non-smooth image denosing problems. The existence and uniqueness of solution for the variational model are proved. Moreover, a Split-Bregman(S-B) based numerical algorithm to solve this model is also proposed to validate the theoretical results. Numerical tests show that the proposed model can produce competitive denoising result to the other three published models.

Suggested Citation

  • Li, Xiao & Meng, Xiaoying & Xiong, Bo, 2022. "A fractional variational image denoising model with two-component regularization terms," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    • Handle: RePEc:eee:apmaco:v:427:y:2022:i:c:s0096300322002521
    DOI: 10.1016/j.amc.2022.127178
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322002521
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127178?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. Tingting Wu, 2016. "Variable Splitting Based Method for Image Restoration with Impulse Plus Gaussian Noise," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, November.
    2. Shama, Mu-Ga & Huang, Ting-Zhu & Liu, Jun & Wang, Si, 2016. "A convex total generalized variation regularized model for multiplicative noise and blur removal," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 109-121.
    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. Wang, Jian & Han, Ziwei & Jiang, Wenjing & Kim, Junseok, 2023. "A fast, efficient, and explicit phase-field model for 3D mesh denoising," Applied Mathematics and Computation, Elsevier, vol. 458(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. Zhao, Xueqing & Huang, Keke & Wang, Xiaoming & Shi, Meihong & Zhu, Xinjuan & Gao, Quanli & Yu, Zhaofei, 2018. "Reaction–diffusion equation based image restoration," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 588-606.
    2. 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).

    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:427:y:2022:i:c:s0096300322002521. 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.