Unsupervised Restoration of Underwater Structural Crack Images via Physics-Constrained Image Translation and Multi-Scale Feature Retention

Accurate visual inspection of underwater infrastructure, such as bridge piers and retaining walls, is often hindered by severe image degradation due to light attenuation and scattering. This paper introduces an unsupervised enhancement framework tailored for restoring underwater images containing structural cracks. The method combines a physical modeling of underwater light transmission with a deep image translation architecture that operates without requiring paired training samples. To address the loss of fine structural details, this paper incorporates a multi-scale feature integration module and a region-focused discriminator that jointly guide the enhancement process. Moreover, a physics-guided loss formulation is designed to promote optical consistency and texture fidelity during training. The proposed approach is validated on a real-world dataset collected from submerged structures under varying turbidity and illumination levels. Both objective evaluations and visual results show substantial improvements over baseline models, with better preservation of crack boundaries and overall visual quality. This work provides a robust solution for preprocessing underwater imagery in structural inspection tasks.