Physical Degradation-Aware Generative Prior Optimization for Zero-Shot Underwater Image Restoration

Single-image underwater restoration is ill-posed: wavelength-dependent attenuation and scattering produce spatially varying structural and chromatic degradation. Learned underwater restoration methods typically depend on training across underwater image collections, whereas generic generative priors provide no explicit guidance for such nonuniform degradation. We propose Physical Degradation-Aware Generative Prior Optimization (PD-GPO), a zero-shot framework that uses a spatial and a chromatic degradation field to guide image-specific latent optimization under a frozen generative prior. A physical degradation field estimator (PDFE), trained only on synthetic degradations of natural RGB-D images, remains frozen and estimates both fields from each generated candidate. The spatial field measures the local degree of structural degradation; it guides contrast initialization and selects the support on which the chromatic field regularizes channel ratios. A frozen DINOv2 feature-consistency loss limits generative drift. We also introduce MIUD, an 850-image multi-region in-situ underwater benchmark spanning depths from several meters to over 4,000 m in the Beibu Gulf, the South China Sea, and the Eastern Indian Ocean. Extensive experiments demonstrate that PD-GPO outperforms existing zero-shot methods on most evaluated metrics.