Underwater Image Denoising and Enhancement via Diffusion Models and Attention Mechanisms

Underwater images are widely used in marine science, ocean engineering, and underwater robotics. However, challenges such as insufficient lighting, scattering, and absorption often degrade image quality, limiting their effectiveness. To address this, we propose AquaDiffusionNet, a model combining Diffusion Models and Attention Mechanisms for underwater image denoising and enhancement. Built on the U-Net architecture, it removes complex noise using diffusion models and captures critical features through multi-head attention mechanisms. AquaDiffusionNet achieves simultaneous denoising and detail enhancement, improving image contrast and clarity. Experiments on public datasets like UIEB show significant performance gains, with PSNR increased by 3.0 dB and SSIM by 0.07, outperforming traditional methods. This model enhances visual quality and provides superior input for subsequent image analysis, driving advancements in underwater vision technology.