Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)

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MarineEye: A Comparative Study on Underwater image Quality

Authors
Ashutosh Pandey1, *, Vani Vats1, Rishi Tripathi1, Smarth Kochar1, Anubhi Bansal1
1Department of Computer Science and Engineering, JIMS Engineering Management Technical Campus, Greater Noida, Uttar Pradesh, India
*Corresponding author. Email: ashutoshpandeyapy@gmail.com
Corresponding Author
Ashutosh Pandey
Available Online 28 May 2026.
DOI
10.2991/978-94-6239-674-6_9How to use a DOI?
Keywords
Underwater Imaging; Image Enhancement Evaluation; Deep Learning; GAN; Image Resolution; CNN
Abstract

Marine exploration, like underwater archaeology, ocean exploration, industrial inspection, marine research, and rescue operations, faces issues due to inaccurate visual analysis. Underwater images face critical challenge due to light absorption, scattering of light, and color distortion in aquatic environments, causing degraded image quality, low contrast, and loss of details. The MarineEye project aims at a systematic comparative analysis of existing enhancement techniques, including classical image processing methods (e.g., Histogram Equalization, CLAHE, Retinex), deep learning-based approaches (e.g., WaterNet, UWCNN), and GAN-based models (e.g., UWCycleGAN, FUnIE-GAN), without developing a new model. Performance is evaluated using standardized metrics: Peak Signal-to-Noise Ratio (PSNR) for noise reduction, Structural Similarity Index Measure (SSIM) for structural fidelity, underwater color image quality evaluation (UCIQE) for color and saturation, and Underwater Image Quality Measure (UIQM) for overall sharpness and contrast. In this study, we develop a comparison matrix to guide the selection of techniques for specific use cases, enhancing decision-making for practical underwater imaging applications.

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 Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
Series
Advances in Engineering Research
Publication Date
28 May 2026
ISBN
978-94-6239-674-6
ISSN
2352-5401
DOI
10.2991/978-94-6239-674-6_9How 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  - Ashutosh Pandey
AU  - Vani Vats
AU  - Rishi Tripathi
AU  - Smarth Kochar
AU  - Anubhi Bansal
PY  - 2026
DA  - 2026/05/28
TI  - MarineEye: A Comparative Study on Underwater image Quality
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 87
EP  - 98
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_9
DO  - 10.2991/978-94-6239-674-6_9
ID  - Pandey2026
ER  -