IDEAS home Printed from https://ideas.repec.org/a/hin/jnlaaa/912373.html
   My bibliography  Save this article

A Convex Adaptive Total Variation Model Based on the Gray Level Indicator for Multiplicative Noise Removal

Author

Listed:
  • Gang Dong
  • Zhichang Guo
  • Boying Wu

Abstract

This paper focuses on the problem of multiplicative noise removal. Using a gray level indicator, we derive a new functional which consists of the adaptive total variation term and the global convex fidelity term. We prove the existence, uniqueness, and comparison principle of the minimizer for the variational problem. The existence, uniqueness, and long-time behavior of the associated evolution equation are established. Finally, experimental results illustrate the effectiveness of the model in multiplicative noise reduction. Different from the other methods, the parameters in the proposed algorithms are found dynamically.

Suggested Citation

  • Gang Dong & Zhichang Guo & Boying Wu, 2013. "A Convex Adaptive Total Variation Model Based on the Gray Level Indicator for Multiplicative Noise Removal," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-21, June.
    • Handle: RePEc:hin:jnlaaa:912373
    DOI: 10.1155/2013/912373
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/AAA/2013/912373.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/AAA/2013/912373.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/912373?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlaaa:912373. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.