Efficient Attentional Underwater Image Enhancement Generative Adversarial Network

In the domain of underwater imaging, the attenuation of light, varying light conditions, and suspended particles often degrade image clarity, resulting in a loss of visual information and color distortion. The proposed Efficient Attentional Underwater Image Enhancement Generative Adversarial Network (EAU-GAN) offers a novel approach to address these challenges by leveraging the power of attention mechanisms in deep learning. This model employs an attention-driven generator that adaptively focuses on critical regions in the underwater image, thereby effectively highlighting obscured details while suppressing unnecessary noise. The discriminator, trained adversarially with the generator, ensures the generation of high-quality, naturalistic images. The innovation of EAU-GAN lies in its attention-driven generator that adaptively concentrates on crucial regions within underwater imagery. This targeted focus enhances obscured details and attenuates extraneous noise, markedly improving image clarity and color fidelity. Experimental results show that EAU-GAN outperforms other methods in both qualitative and quantitative aspects.