IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8535206.html
   My bibliography  Save this article

Rician Noise Removal via a Learned Dictionary

Author

Listed:
  • Jian Lu
  • Jiapeng Tian
  • Lixin Shen
  • Qingtang Jiang
  • Xueying Zeng
  • Yuru Zou

Abstract

This paper proposes a new effective model for denoising images with Rician noise. The sparse representations of images have been shown to be efficient approaches for image processing. Inspired by this, we learn a dictionary from the noisy image and then combine the MAP model with it for Rician noise removal. For solving the proposed model, the primal-dual algorithm is applied and its convergence is studied. The computational results show that the proposed method is promising in restoring images with Rician noise.

Suggested Citation

  • Jian Lu & Jiapeng Tian & Lixin Shen & Qingtang Jiang & Xueying Zeng & Yuru Zou, 2019. "Rician Noise Removal via a Learned Dictionary," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, February.
    • Handle: RePEc:hin:jnlmpe:8535206
    DOI: 10.1155/2019/8535206
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2019/8535206.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2019/8535206.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/8535206?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:jnlmpe:8535206. 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.