IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v54y2013i2p263-282.html
   My bibliography  Save this article

Preconditioned iterative regularization in Banach spaces

Author

Listed:
  • Paola Brianzi
  • Fabio Di Benedetto
  • Claudio Estatico

Abstract

Regularization methods for inverse problems formulated in Hilbert spaces usually give rise to over-smoothness, which does not allow to obtain a good contrast and localization of the edges in the context of image restoration. On the other hand, regularization methods recently introduced in Banach spaces allow in general to obtain better localization and restoration of the discontinuities or localized impulsive signals in imaging applications. We present here an expository survey of the topic focused on the iterative Landweber method. In addition, preconditioning techniques previously proposed for Hilbert spaces are extended to the Banach setting and numerically tested. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Paola Brianzi & Fabio Di Benedetto & Claudio Estatico, 2013. "Preconditioned iterative regularization in Banach spaces," Computational Optimization and Applications, Springer, vol. 54(2), pages 263-282, March.
    • Handle: RePEc:spr:coopap:v:54:y:2013:i:2:p:263-282
    DOI: 10.1007/s10589-012-9527-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-012-9527-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-012-9527-2?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.

    Citations

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


    Cited by:

    1. Cornelio, Anastasia & Porta, Federica & Prato, Marco, 2015. "A convergent least-squares regularized blind deconvolution approach," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 173-186.
    2. Bonettini, Silvia & Prato, Marco & Rebegoldi, Simone, 2016. "A cyclic block coordinate descent method with generalized gradient projections," Applied Mathematics and Computation, Elsevier, vol. 286(C), pages 288-300.

    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:spr:coopap:v:54:y:2013:i:2:p:263-282. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.