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

Zinan Li; Yiming Li; Weikang Chen; Chaobo Zhang; Mingfeng Chen; Xiaohao Wang; Weihua Gui; Xiaojun Liang

doi:10.1117/12.3036424

20 November 2024 DSAS-S2APNet: a dual-stage auxiliary supervision network for single-frame to absolute phase prediction

Zinan Li, Yiming Li, Weikang Chen, Chaobo Zhang, Mingfeng Chen, Xiaohao Wang, Weihua Gui, Xiaojun Liang

Proceedings Volume 13241, Optical Metrology and Inspection for Industrial Applications XI; 1324112 (2024) https://doi.org/10.1117/12.3036424
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zinan Li, Yiming Li, Weikang Chen, Chaobo Zhang, Mingfeng Chen, Xiaohao Wang, Weihua Gui, and Xiaojun Liang "DSAS-S2APNet: a dual-stage auxiliary supervision network for single-frame to absolute phase prediction", Proc. SPIE 13241, Optical Metrology and Inspection for Industrial Applications XI, 1324112 (20 November 2024); https://doi.org/10.1117/12.3036424

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KEYWORDS

Phase unwrapping

Deep learning

3D metrology

Education and training

Network architectures

Semantics

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