Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Under Water Image Enhancement For Marine Life Monitoring

Authors
P. L. V. S. Praveen1, *, M. S. S. Sasi Kumar2, K. Mukesh Chowdary3
1Undergraduate Student Dept of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institue of Science and Technology, Chennai, India
2Assistant Professor (Senior Grade) Dept of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institue of Science and Technology, Chennai, India
3Undergraduate Student Dept of CSE, Vel Tech Rangarajan Dr.Sagunthala R&D Institue of Science and Technology, Chennai, India
*Corresponding author. Email: praveenparimi1421@gmail.com
Corresponding Author
P. L. V. S. Praveen
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_61How to use a DOI?
Keywords
Enhancing Underwater Images; Underwater marine life monitoring; color correction; deep learning; convolutional neural networks (CNNs); image restoration; image visualization; marine imaging; marine ecosystem analysis
Abstract

Images captured underwater have low contrast, distorted colors, and limited visibility because of light attenuation and scattering in water. This poses a huge challenge in marine organisms monitoring, where species recognition, categorization, and behavior evaluation are performed. We propose a deep learning-driven underwater image enhancement model combining FunieGAN for image processing and YOLOv8 for marine species recognition. The proposed model enhances the color accuracy, contrast, and structures of the images captured. These images are then used for precise identification and categorization of the marine species. The effectiveness of the proposed model is determined based on quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Underwater Image Quality Measure (UIQM). The experimental results reveal the superiority of our model over existing methods regarding both image enhancement and species recognition.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_61How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. L. V. S. Praveen
AU  - M. S. S. Sasi Kumar
AU  - K. Mukesh Chowdary
PY  - 2026
DA  - 2026/06/16
TI  - Under Water Image Enhancement For Marine Life Monitoring
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 615
EP  - 626
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_61
DO  - 10.2991/978-94-6239-693-7_61
ID  - Praveen2026
ER  -