Recuperación de señales interferométricas sobresaturadas impulsada por aprendizaje profundo para la medición continua...

To address signal oversaturation caused by the limited dynamic range of photodetectors in optical frequency comb (OFC) time-stretch spectral interferometry, we design a Uformer 1D network that integrates U-Net and Transformer architectures for signal recovery. The model is trained via a three-stage strategy: large-scale ideal-simulation pretraining, real paired-data fine-tuning with frequency-domain and total variation losses, and self-supervised learning on unlabeled real signals to enhance generalization. Experiments show that the recovered signals achieve a mean measurement error of ∼0.2 µm, with final average log-RMSEs of 0.065 (training) and 0.057 (validation), extending the effective dynamic range from 10 dBm to 15 dBm. Real-time surface reconstruction of a one-yuan coin further demonstrates its potential for continuous, real-time, high-dynamic-range 3D morphology measurement.