MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility

Abstract

The underwater environment presents unique challenges (color distortions, reduced contrast, blurriness) hindering accurate analysis. This work introduces MuLA-GAN, a novel approach leveraging Generative Adversarial Networks (GANs) and specifically adapted Multi-Level Attention for comprehensive underwater image enhancement. MuLA-GAN integrates Multi-Level Attention within the GAN architecture to prioritize learning discriminative features crucial for precise image restoration. These relevant features encompass information on local details within image regions leveraged by spatial attention and features at various scales across the entire image captured by multi-level attention. This allows MuLA-GAN to identify and enhance objects, textures, and edges obscured by underwater distortions while also reconstructing a more accurate and visually clear representation of the underwater scene by analyzing low-level information like edges and textures, as well as high-level information like object shapes and global scene information. By selectively focusing on these relevant features, MuLA-GAN excels at capturing and preserving intricate details in underwater imagery, which is essential for various marine research, exploration, and resource management applications. Extensive evaluations on diverse datasets (UIEB test, UIEB challenge, U45, UCCS) demonstrate MuLA-GAN's superior performance compared to existing methods. Additionally, a specialized bio-fouling and aquaculture dataset confirms the model's robustness in challenging environments. On the UIEB test dataset, MuLA-GAN achieves exceptional Peak Signal-to-Noise Ratio (PSNR) (25.59) and Structural Similarity Index (SSIM) (0.893) scores, surpassing Water-Net (24.36 PSNR, 0.885 SSIM). This work addresses a significant research gap in underwater image enhancement by demonstrating the effectiveness of combining GANs with specifically adapted Multi-Level Attention mechanisms. This tailored approach offers a novel and comprehensive framework for restoring underwater image quality, providing valuable insights for accurate underwater scene analysis. The source code for MuLA-GAN is publicly available on GitHub at https://github.com/AhsanBaidar/MuLA_GAN.git