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

作者：Chao Dong, Zhenming Li, Wenbo Zhu, Haibing Li, Bing Zheng, Zhongbo Zhang, Weijie Fu · 发表于：Mathematics · 年份：2024 · DOI：10.3390/math12101551 · 被引用次数：7 · 研究领域：Image Enhancement Techniques、Advanced Image Fusion Techniques、Image and Signal Denoising Methods

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...