Li Ziqiang, Li Xinyang, Liang Rongguang
Opt Express. 2020 Aug 17;28(17):24747-24760. doi: 10.1364/OE.397904.
A two-frame phase-shifting interferometric wavefront reconstruction method based on deep learning is proposed. By learning from a large number of simulation data based on a physical model, the wrapped phase can be calculated accurately from two interferograms with an unknown phase step. The phase step can be any value excluding the integral multiples of π and the size of interferograms can be flexible. This method does not need a pre-filtering to subtract the direct-current term, but only needs a simple normalization. Comparing with other two-frame methods in both simulations and experiments, the proposed method can achieve better performance.
提出了一种基于深度学习的双帧相移干涉波前重建方法。通过从基于物理模型的大量仿真数据中学习，可以从具有未知相位步长的两幅干涉图中准确计算出包裹相位。相位步长可以是除π的整数倍以外的任何值，干涉图的大小可以灵活变化。该方法不需要进行预滤波来减去直流项，只需要进行简单的归一化处理。在仿真和实验中与其他双帧方法进行比较，该方法能够取得更好的性能。
