Open AccessRandom two-frame interferometry based on deep learning
Author(s) -
Ziqiang Li,
Xinyang Li,
Rongguang Liang
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.397904
Subject(s) - wavefront , interferometry , normalization (sociology) , computer science , optics , frame (networking) , phase (matter) , phase unwrapping , algorithm , physics , telecommunications , quantum mechanics , sociology , anthropology
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.