IDEAS home Printed from https://ideas.repec.org/a/ids/ijenma/v15y2024i1p44-59.html
   My bibliography  Save this article

Hybrid sparse and block-based compressive sensing algorithm for industry based applications

Author

Listed:
  • R. Sekar
  • N. Manikanda Devarajan
  • G. Ravi
  • B. Rajasekaran
  • S. Chidambaram

Abstract

Image reconstructions are a challenging task in MRI images. The performance of the MRI image can be measure by following parameters like mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Compromising the above parameters and reconstructing the MRI image leads to false diagnosing. To avoid the false diagnosis, we have combined sparse based compressive sensing and block-based compressive sensing algorithm, and we introduced the hybrid sparse and block-based compressive sensing algorithm (HSBCS). In compressive stage, however, image reconstruction performance is decreased, hence, in the image reconstruction module, we have introduced convex relaxation algorithm. This proposed algorithm is obtained by relaxing some of the constraints of the original problem and meanwhile extending the objective function to the larger space. The performance is compared with the existing algorithm, block-based compressive sensing algorithm (BCS), BCS based on discrete wavelet transform (DWT), and sparse based compress-sensing algorithm (SCS). The experimentation is carried out using BRATS dataset, and the performance of image compression HSBCS evaluated based on SSIM, and PSNR, which attained 56.19 dB, and 0.9812.

Suggested Citation

  • R. Sekar & N. Manikanda Devarajan & G. Ravi & B. Rajasekaran & S. Chidambaram, 2024. "Hybrid sparse and block-based compressive sensing algorithm for industry based applications," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 15(1), pages 44-59.
    • Handle: RePEc:ids:ijenma:v:15:y:2024:i:1:p:44-59
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=137432
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijenma:v:15:y:2024:i:1:p:44-59. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=187 .

    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.