IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i5d10.1007_s13198-022-01665-7.html
   My bibliography  Save this article

CDF based dual transform approach for UAV video visual enhancement in RGB model

Author

Listed:
  • Ashish Srivastava

    (Madan Mohan Malaviya University of Technology)

  • Jay Prakash

    (Madan Mohan Malaviya University of Technology)

Abstract

Unavoidable noise can be seen in video and photos taken by advanced cameras mounted to UAVs due to sensors attached, height concerns, or hardware faults. Noise is a major contributor to the degradation of both video and image quality. Both color and luminance are examples of noise. With no additional noise added, the suggested algorithm’s effectiveness was verified by discovering SSI, MSE, NRMSE, SNR, and PSNR on UAV RGB color noise. This algorithm RGB model for UAV video noise reduction gives a 5.9–15.4% increase in PSNR, a 7.8–29.4% increase in SNR, and a 32–50% improvement in NRMSE compared to current state-of-the-art noise suppression methods. An algorithm with this approach is the first in UAV video processing with better results in the calculation domain of research knowledge. As a final point, we offer up an assortment of enticing goals for further investigation.

Suggested Citation

  • Ashish Srivastava & Jay Prakash, 2022. "CDF based dual transform approach for UAV video visual enhancement in RGB model," 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. 13(5), pages 2559-2571, October.
  • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01665-7
    DOI: 10.1007/s13198-022-01665-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01665-7
    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/s13198-022-01665-7?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:13:y:2022:i:5:d:10.1007_s13198-022-01665-7. 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.