$\pi$-SUB: A Physics-Informed Synthetic Underwater Benchmark Dataset for Underwater Image Enhancement 文章

arXiv:2608.10589v1 Announce Type: new Abstract: This paper presents $\pi$-SUB, a physics-informed framework for generating synthetic underwater benchmark datasets that bridges the synthetic-to-real gap for Underwater Image Enhancement (UIE). The proposed framework extends the classical underwater image formation model by incorporating depth-dependent downwelling irradiance, biologically resolved absorption, and environmental scattering across all ten Jerlov water types, together with independently controllable residual phenomena. Using this framework, the $\pi$-SUB dataset consists of paired synthetic underwater-reference images spanning shallow-to-deep and coastal-to-oceanic environments. Extensive simulation studies have been carried out to evaluate $\pi$-SUB along two criteria namely hyper-realism and generalizability. For hyper-realism, $\pi$-SUB attains a global Frechet Inception Distance (FID) that is 46% lower than Syrea.