Underwater Image Enhancement Based on Hybrid Attention Mechanism and Transformer

To address issues in underwater images, such as blurriness, low contrast, and color distortion, we propose a Generative Adversarial Network (GAN) based on a hybrid attention mechanism and Transformer, named HAT-GAN. HAT-GAN is designed to efficiently recover image details and improve visual quality. The proposed framework incorporates a Multi-Domain Attention Transformer, which processes query information in the frequency domain while handling key-value information in the spatial domain to generate attention maps with high information density. Additionally, in the up-sampling and down-sampling modules, different attention mechanisms are applied at different sampling stages to acquire richer information. Finally, in the discriminator, a Dual Attention mechanism is employed, enabling the discriminator to focus on critical regions of the image. Through experimental comparisons and ablation studies, we show that our approach successfully mitigates issues such as image detail blur and color distortion in underwater images, producing visually pleasing results.