AMSMC-UGAN: Adaptive Multi-Scale Multi-Color Space Underwater Image Enhancement with GAN-Physics Fusion

Dong Chao, Zhenming Li, Wenbo Zhu (), Haibing Li, Bing Zheng, Zhongbo Zhang and Weijie Fu
Additional contact information
Dong Chao: Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
Zhenming Li: College of Mechanical Engineering and Automation, Foshan University, Foshan 528200, China
Wenbo Zhu: College of Mechanical Engineering and Automation, Foshan University, Foshan 528200, China
Haibing Li: College of Mechanical Engineering and Automation, Foshan University, Foshan 528200, China
Bing Zheng: Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
Zhongbo Zhang: College of Mechanical Engineering and Automation, Foshan University, Foshan 528200, China
Weijie Fu: College of Mechanical Engineering and Automation, Foshan University, Foshan 528200, China

Mathematics, 2024, vol. 12, issue 10, 1-19

Abstract: Underwater vision technology is crucial for marine exploration, aquaculture, and environmental monitoring. However, the challenging underwater conditions, including light attenuation, color distortion, reduced contrast, and blurring, pose difficulties. Current deep learning models and traditional image enhancement techniques are limited in addressing these challenges, making it challenging to acquire high-quality underwater image signals. To overcome these limitations, this study proposes an approach called adaptive multi-scale multi-color space underwater image enhancement with GAN-physics fusion (AMSMC-UGAN). AMSMC-UGAN leverages multiple color spaces (RGB, HSV, and Lab) for feature extraction, compensating for RGB’s limitations in underwater environments and enhancing the use of image information. By integrating a membership degree function to guide deep learning based on physical models, the model’s performance is improved across different underwater scenes. In addition, the introduction of a multi-scale feature extraction module deepens the granularity of image information, learns the degradation distribution of different image information of the same image content more comprehensively, and provides useful guidance for more comprehensive data for image enhancement. AMSMC-UGAN achieved maximum scores of 26.04 dB, 0.87, and 3.2004 for PSNR, SSIM, and UIQM metrics, respectively, on real and synthetic underwater image datasets. Additionally, it obtained gains of at least 6.5%, 6%, and 1% for these metrics. Empirical evaluations on real and artificially distorted underwater image datasets demonstrate that AMSMC-GAN outperforms existing techniques, showcasing superior performance with enhanced quantitative metrics and strong generalization capabilities.

Keywords: underwater image enhancement; multi-color space; multi-scale; adaptive; GAN-physics fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/10/1551/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/10/1551/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:10:p:1551-:d:1395698

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().