  The underwater environment presents unique challenges, including color
distortions, reduced contrast, and blurriness, hindering accurate analysis. In
this work, we introduce MuLA-GAN, a novel approach that leverages the
synergistic power of Generative Adversarial Networks (GANs) and Multi-Level
Attention mechanisms for comprehensive underwater image enhancement. The
integration of Multi-Level Attention within the GAN architecture significantly
enhances the model's capacity to learn discriminative features crucial for
precise image restoration. By selectively focusing on relevant spatial and
multi-level features, our model excels in capturing and preserving intricate
details in underwater imagery, essential for various applications. Extensive
qualitative and quantitative analyses on diverse datasets, including UIEB test
dataset, UIEB challenge dataset, U45, and UCCS dataset, highlight the superior
performance of MuLA-GAN compared to existing state-of-the-art methods.
Experimental evaluations on a specialized dataset tailored for bio-fouling and
aquaculture applications demonstrate the model's robustness in challenging
environmental conditions. On the UIEB test dataset, MuLA-GAN achieves
exceptional PSNR (25.59) and SSIM (0.893) scores, surpassing Water-Net, the
second-best model, with scores of 24.36 and 0.885, respectively. This work not
only addresses a significant research gap in underwater image enhancement but
also underscores the pivotal role of Multi-Level Attention in enhancing GANs,
providing a novel and comprehensive framework for restoring underwater image
quality.
