The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130802
PDF

Severely Degraded Underwater Image Enhancement with a Wavelet-based Network

Author 1: Shunsuke Takao
Author 2: Tsukasa Kita
Author 3: Taketsugu Hirabayashi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Underwater images are important in marine science and ocean engineering fields owing to giving color information, low cost, and compact. Yet obtained underwater images are often degraded and restoring and enhancing wavelength selective signal attenuation of underwater images depending on complex underwater physical process is essential in practical application. While recently developed deep learning is a promising choice, constructing sufficiently large dataset covering whole real images is challenging, peculiar to underwater image processing. In order to supplement relatively small dataset, previous studies alternatively construct an artificial underwater image dataset based on a physical model or Generative Adversarial Network. Also, incorporating traditional signal processing methods into the network architecture has shown promising success, though enhancement of severely degraded underwater images remains to be a big issue. In this paper, we tackle underwater image enhancement based on an encoder-decoder based deep learning model incorporating discrete wavelet transform and whitening and coloring transform. We also construct a severely degraded real underwater image dataset. The presented model shows excellent results both qualitatively and quantitatively in the artificial and real image dataset. Constructed dataset is available at https://github.com/tkswalk/2022-IJACSA.

Keywords: Underwater image enhancement; deep learning; discrete wavelet transform; whitening and coloring transform

Shunsuke Takao, Tsukasa Kita and Taketsugu Hirabayashi, “Severely Degraded Underwater Image Enhancement with a Wavelet-based Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130802

@article{Takao2022,
title = {Severely Degraded Underwater Image Enhancement with a Wavelet-based Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130802},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130802},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Shunsuke Takao and Tsukasa Kita and Taketsugu Hirabayashi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org