This repository contains code for the paper A fast combined-frequency phase extraction for phase shifting profilometry. In this work, we formulate the phase extraction problem with high-order harmonic as a maximum likelihood estimation (MLE), and our CFPE is an efficient optimization method by introducing a latent phase map and incorporating the expectation-maximization (EM) framework. Compared to the only high-order baseline (LLS), our CFPE method only needs ** about 5% execution time ** to achieve high-order accuracy.
As a special curve fitting problem, our CFPE utilizes more data points (cross-frequency images) to solve a high-order harmonic model. That says,
Our CFPE reports an efficient iterative solution to this problem
conda install numpy matplotlib opencv seaborn
conda install -c anaconda pathlib
- Exp1.ipynb: Test on several synthetic PSP images;
- Exp2.ipynb: Test on 4 real PSP cases;
-
Exp3.ipynb: Compare the two interesting cases, with the same gamma distortion (
$\gamma$ =1.3),- Our CFPE method with 3-frequency 3-step images (periods T1=33, T2=36, T3=39);
- Standard PE method with 1-frequency 9-step images (T=1920);
@article{lee2022cfpe,
author={Lee, Yong and Mao, Ya and Chen, Zuobing},
journal={Optics Express},
title={Fast combined-frequency phase extraction for phase shifting profilometry},
year={2022},
volume={30},
number={25},
pages={45288--45300},
doi={https://doi.org/10.1364/OE.473513}}
For any questions regarding this work, please email me at [email protected], [email protected].
These works are contributed to our CFPE project,