IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-031-99219-3_23.html

Improving Image Denoising Performance With CNN-Attention-Like Encoder Layers

In: Advancement in Embedded and Mobile Systems

Author

Listed:
  • Gladys Mange

    (KCA University, School of Technology (SOT)
    Jomo Kenyatta University of Agriculture and Technology)

  • Jorge Marx Gómez

    (Oldenburg University)

  • Ronald Waweru

    (Jomo Kenyatta University of Agriculture and Technology)

  • Michael Kimwele

    (Jomo Kenyatta University of Agriculture and Technology)

Abstract

Image denoising is a fundamental task in computer vision, critical for improving image quality in a variety of applications. This research presents a novel technique for image denoising that employs dual Convolutional Neural Network (CNN) encoders and attention-based decoders. This research uses the strengths of attention mechanisms to selectively reconstitute features retrieved by encoders, improving the quality of denoised images. Furthermore, it offers a method for combining attention maps from various encoders to improve the denoising process. In terms of objective quality and its capacity to reduce noise, the CNN with Attention (CNWATT2) denoising technique performs better than the previously employed denoising models.

Suggested Citation

  • Gladys Mange & Jorge Marx Gómez & Ronald Waweru & Michael Kimwele, 2026. "Improving Image Denoising Performance With CNN-Attention-Like Encoder Layers," Progress in IS, in: Jorge Marx Gómez & Antoine Gatera & Devotha Godfrey Nyambo (ed.), Advancement in Embedded and Mobile Systems, pages 337-349, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-99219-3_23
    DOI: 10.1007/978-3-031-99219-3_23
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:spr:prochp:978-3-031-99219-3_23. 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.