IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i4d10.1007_s13198-019-00887-6.html
   My bibliography  Save this article

Multi-focus image fusion via NSST with non-fixed base dictionary learning

Author

Listed:
  • Yong Yang

    (Jiangxi University of Finance and Economics)

  • Yingmei Zhang

    (Jiangxi University of Finance and Economics)

  • Shuying Huang

    (Jiangxi University of Finance and Economics)

  • Jiahua Wu

    (Jiangxi University of Finance and Economics)

Abstract

Multi-focus image fusion method can fuse images taken from the same view point with different focal settings, and obtain an image with every object in focus. In this paper, a novel multi-focus image fusion via non-subsampled shearlet transform (NSST) with non-fixed base dictionary learning is presented. First, low frequency coefficients and high frequency coefficients are obtained by NSST. Then, a new strategy, which can enhance the information of spatial detail for the fused image is proposed to process two different coefficients. The low frequency coefficients are fused via a non-fixed base dictionary, which makes the K-SVD algorithm more efficient, and the high frequency coefficients are fused with spatial frequency, which is effective in the fused image. Experiment results demonstrate that the results of proposed method obtain more spatial details and have almost zero residuals compared with several conventional methods in terms of both visual quality and objective measurements.

Suggested Citation

  • Yong Yang & Yingmei Zhang & Shuying Huang & Jiahua Wu, 2020. "Multi-focus image fusion via NSST with non-fixed base dictionary learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 849-855, August.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:4:d:10.1007_s13198-019-00887-6
    DOI: 10.1007/s13198-019-00887-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-019-00887-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-019-00887-6?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:spr:ijsaem:v:11:y:2020:i:4:d:10.1007_s13198-019-00887-6. 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.