Underwater Image Enchancement using CNN based Learning Methods

Underwater images find application in various fields, like marine research, inspection of aquatic habitat, underwater surveillance, identification of minerals, and more. However, underwater shots are affected a lot during the acquisition process due to the absorption and scattering of light. As depth increases, longer wavelengths get absorbed by water; therefore, the images appear predominantly bluish-green, and red gets absorbed due to higher wavelength. These phenomenon’s result in significant degradation of images due to which images have low contrast, colour distortion, and low visibility. Hence, underwater images need enhancement to improve the quality of images to be used for various applications while preserving the valuable information contained in them.

  • Research Type: Applied Research
  • Paper Type: Experimental Research Paper
  • Vol.6 , Issue 6 , Pages: 1 - 6, Nov 2024
  • Published on: 04 Nov, 2024
  • Issue Type: Regular
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  • Cite Score
    :

    100

  • No. of authors
    :

    75

  • No. of Downloads
    :

    43

  • Cite Score
    :

    100

  • No. of authors
    :

    75

  • No. of Downloads
    :

    43

About Authors:
Jada Nageswara Rao
India
Jawaharlal Nehru Technological University Anantapur(JNTUA)

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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: Jada Nageswara Rao, jadanageswararao@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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