Random two-frame interferometry based on deep learning

Ziqiang Li, Xinyang Li, Rongguang Liang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)24747-24760
Number of pages14
JournalOptics Express
Volume28
Issue number17
DOIs
StatePublished - Aug 17 2020

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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