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- W3046703034 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." @default.
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- W3046703034 date "2020-08-07" @default.
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- W3046703034 title "Random two-frame interferometry based on deep learning" @default.
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- W3046703034 doi "https://doi.org/10.1364/oe.397904" @default.
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