Polarization-guided diffusion model for physically inspired underwater image descattering.

Removing scattering is a central challenge in underwater imaging because water induces complex light-matter interactions. Scattering not only blurs image details but also degrades the reliability of downstream vision tasks. Importantly, backscattered light and object-reflected light exhibit distinct polarimetric differences, offering a physical cue for descattering. However, most existing approaches usually rely on a single polarization parameter or assume a constant background polarization degree, limiting their effectiveness in complex scenes. Therefore, an underwater image enhancement network based on a diffusion model and a polarization drive is proposed, which decomposes the problem into two stages: (i) descattering prior generation and (ii) scattering removal. In the prior stage, the diffusion model is introduced for the first time, to our knowledge, to fully mine the polarization information and distill it into high-quality priors that guide accurate scattering suppression. In the removal stage, the illumination estimation module is designed to compensate for water-body absorption and enforce global illumination consistency, thereby improving perceptual naturalness. Extensive experiments demonstrate state-of-the-art quantitative and qualitative performance, providing reliable, high-quality inputs for downstream underwater vision applications.