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Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study
by
Yunsheng Ma
Yunsheng Ma 1,2,†,
Yanan Cheng
Yanan Cheng 3,† and
Dapeng Zhang
Dapeng Zhang 1,*
1
Ship and Maritime College, Guangdong Ocean University, Zhanjiang 524005, China
2
School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
3
College of Business, Taizhou Institute of Sci.&Tech., NJUST, Taizhou 225300, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Mar. Sci. Eng. 2025, 13(5), 899; https://doi.org/10.3390/jmse13050899 (registering DOI)
Submission received: 4 April 2025
/
Revised: 25 April 2025
/
Accepted: 26 April 2025
/
Published: 30 April 2025
Abstract
Underwater remote sensing image enhancement is complicated by low illumination, color bias, and blurriness, affecting deep-sea monitoring and marine resource development. This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing. The physical model, based on the Jaffe–McGlamery model, integrates multi-scale histogram equalization, wavelength compensation, and Laplacian sharpening, using cluster analysis to target enhancements. It performs well in shallow, stable waters (turbidity < 20 NTU, depth < 10 m, PSNR = 12.2) but struggles in complex environments (turbidity > 30 NTU). Deep learning models, including water-net, UWCNN, UWCycleGAN, and U-shape Transformer, excel in dynamic conditions, achieving UIQM = 0.24, though requiring GPU support for real-time use. Evaluated on the UIEB dataset (890 images), the physical model suits specific scenarios, while deep learning adapts better to variable underwater settings. These findings offer a theoretical and technical basis for underwater image enhancement and support sustainable marine resource use.
Share and Cite
MDPI and ACS Style
Ma, Y.; Cheng, Y.; Zhang, D.
Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study. J. Mar. Sci. Eng. 2025, 13, 899.
https://doi.org/10.3390/jmse13050899
AMA Style
Ma Y, Cheng Y, Zhang D.
Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study. Journal of Marine Science and Engineering. 2025; 13(5):899.
https://doi.org/10.3390/jmse13050899
Chicago/Turabian Style
Ma, Yunsheng, Yanan Cheng, and Dapeng Zhang.
2025. "Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study" Journal of Marine Science and Engineering 13, no. 5: 899.
https://doi.org/10.3390/jmse13050899
APA Style
Ma, Y., Cheng, Y., & Zhang, D.
(2025). Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study. Journal of Marine Science and Engineering, 13(5), 899.
https://doi.org/10.3390/jmse13050899
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