Spatial-Frequency Dual-Domain Self-Supervised Image Denoising for Fringe Projection Profilometry

To overcome phase blurring and high-frequency edge degradation in spatial-domain self-supervised fringe denoising, we propose a spatial-frequency dual-domain framework based on Neighbor2Neighbor. By incorporating discrete wavelet transform (DWT) down-sampling and focal frequency loss (FFL), the model effectively suppresses complex optical noise while preserving sharp 2π phase-step boundaries and sinusoidal carrier spectral fidelity without requiring clean labels. Extensive evaluations on synthetic and real-shot optical datasets demonstrate that our method significantly outperforms mainstream self-supervised algorithms in phase reconstruction accuracy and structural restoration, successfully eliminating pseudo-3D surface spikes with a GPU forward inference latency as low as 1.73 ms.