IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v315y2017icp453-467.html
   My bibliography  Save this article

Modification of TV-ROF denoising model based on Split Bregman iterations

Author

Listed:
  • Campagna, Rosanna
  • Crisci, Serena
  • Cuomo, Salvatore
  • Marcellino, Livia
  • Toraldo, Gerardo

Abstract

Minimizing variational models by means of (un)constrained optimization algorithms is a well-known approach for dealing with the image denoising problem. In this paper, we propose a modification of the widely explored TV-ROF model named H-TV-ROF, in which a penalty term based on higher order derivatives is added. A Split Bregman iterative scheme is used to solve the proposed model and its convergence is proved. The performance of the new algorithm is analized and compared with TV-ROF on a set of numerical experiments.

Suggested Citation

  • Campagna, Rosanna & Crisci, Serena & Cuomo, Salvatore & Marcellino, Livia & Toraldo, Gerardo, 2017. "Modification of TV-ROF denoising model based on Split Bregman iterations," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 453-467.
    • Handle: RePEc:eee:apmaco:v:315:y:2017:i:c:p:453-467
    DOI: 10.1016/j.amc.2017.08.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300317305520
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2017.08.001?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.

    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:315:y:2017:i:c:p:453-467. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.