Underwater structured-light 3D imaging method based on FP-DiffNet.

Underwater structured-light three-dimensional (3D) imaging is essential for the precise reconstruction of submerged objects in scientific and engineering applications. However, the observed fringe patterns are often degraded by underwater light attenuation, scattering, and refractive distortion, leading to fringe blurring and aliasing and, consequently, reduced phase-reconstruction accuracy. To address these issues, we analyze the propagation characteristics of structured light in underwater environments and their effects on fringe image quality. With that, we engineer a diffusion-model-based fringe restoration neural framework, referred to as FP-DiffNet, in which fringe restoration is formulated as a probability-driven iterative denoising process. Specifically, a U-Net model is trained to progressively map degraded fringe observations to clear fringe patterns, incorporating physics-guided constraints and an adaptive noise-annealing mechanism to effectively separate scattering-induced noise while preserving fringe structures during the reverse diffusion process. The proposed framework is evaluated in terms of fringe image quality, wrapped-phase accuracy, and 3D reconstruction performance. Under extremely high turbidity, it achieves a PSNR of 42.42 dB and a wrapped-phase MAE of 0.0354 rad. By combining diffusion-driven phase reconstruction with dynamic compensation, sub-millimeter absolute measurement is achieved in turbid water, with a 3D reconstruction RMSE below 0.1 mm. Importantly, no prior modeling of water optical parameters is required, enabling robust and practical underwater 3D imaging.