DA-GAN: Dual-Attention GAN for Underwater Image Enhancement With Contrast and Color Correction

Underwater images typically suffer from severe color distortion, low contrast, and poor visibility due to light absorption and scattering in aquatic environments. To address these challenges, we propose a novel Dual-Attention GAN (DA-GAN), specifically designed to enhance contrast and correct color distortions in underwater images. DA-GAN features three key innovations: a Hybrid CNN-Attention Bottleneck Layer (HCABL) for efficient enhancement of local and global features, a Transformer-based Channel Attention Module (TCAM) for color distortion removal, and a Multi-Color Space Loss function to improve contrast and visibility. HCABL employs parallel attention mechanisms and depth-wise separable convolutions to enhance both global context and local spatial details. TCAM enhances the model’s sensitivity to various color channels, enabling effective correction of complex color distortions. Furthermore, the Multi-Color Space Loss function, tailored for the HSV color space and the image’s bright and dark channels, further enhance contrast and visibility. Comprehensive ablation studies validate the effectiveness of each component, while extensive comparison experiments demonstrate that DA-GAN achieves state-of-the-art or comparable performance across six public underwater image datasets (SUIM, UIEB, EUVP, LSUI, UCCS and SQUID). The source code is publicly available at https: //github.com/littlebearRedgood/DAGAN.