Translucent materials are widely used in micro-optical components, and related optically transmissive structures, where surface three-dimensional morphology directly influences optical transmission efficiency and interfacial reliability. Struc-tured Illumination Microscopy (SIM), with its optical sectioning capability, is an important technique for 3D measurement of transparent and translucent materials. However, their inherently low reflectivity often reduces fringe modulation contrast, making weak fringes difficult to resolve. Moreover, refractive-index inhomogeneity can induce uncontrolled fringe phase shifts, which further amplify fringe frequency deviations caused by DMD projection offsets and optical-path misalignment, degrading 3D reconstruction stability and limiting high-throughput, real-time inspection. To address these issues, this study proposes a single-frame U-Net reconstruction framework integrating a Squeeze-and-Excitation (SE) module and a Transformer Bottleneck (TB) for global dependency modeling. A physics-informed Frequency Perturbation Augmentation (FPA) strategy is further introduced to improve weak-fringe parsing and reconstruction robustness. Trained on 8, 542 pairs of structured illumination and optically sectioned images, the proposed method significantly improves imaging quality while reducing reconstruction time, achieving about an eightfold acceleration. It provides an efficient, scalable solution for high-throughput 3D surface morphology reconstruction of transparent and translucent materials under weakly modulated imaging conditions.

structured illumination microscopy, transparent and translucent materials, 3D surface metrology, U-Net, attention mechanism