DSAS-S2APNet: a dual-stage auxiliary supervision network for single-frame to absolute phase prediction

Single-frame high-precision 3D measurement using deep learning has been widely studied for its minimal measurement time. However, the long physical and semantic distances make the end-to-end absolute phase reconstruction of single-frame grating challenging. To tackle this difficulty, we propose the DSAS-S2AP-X (Dual-Stage Auxiliary Supervision Network for Single-Frame to Absolute Phase Prediction with X) strategy, which includes the secondary highest frequency unwrapped phase and the highest frequency wrapped phase supervision branches. It combines a multi-frequency temporal phase unwrapping model with existing regression networks X (meaning arbitrary). Experimental results have shown that the DSAS-S2AP-ResUNet34 strategy can reduce the mean absolute error (MAE) and root mean square error (RMSE) of the absolute phase by 34.3% and 25.9% respectively based on the ResUNet34.