期刊
OPTICS EXPRESS
卷 28, 期 15, 页码 21692-21703出版社
OPTICAL SOC AMER
DOI: 10.1364/OE.398492
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资金
- Jiangsu Provincial Key Research and Development Program [BE2018126]
- Fundamental Research Funds for the Central Universities [30920031101]
- National Natural Science Foundation of China [61727802, 61971227]
Fringe projection profilometry (i.e., FPP) has been one of the most popular 3-D measurement techniques. The phase error due to system random noise becomes non-ignorable when fringes captured by a camera have a low fringe modulation, which are inevitable for objects' surface with un-uniform reflectivity. The phase calculated from these low-modulation fringes may have a non-ignorable phase error and generate 3-D measurement error. Traditional methods reduce the phase error with losing details of 3-D shapes or sacrificing the measurement speed. In this paper, a deep learning-based fringe modulation-enhancing method (i.e., FMEM) is proposed, that transforms two low-modulation fringes with different phase shifts into a set of three phase-shifted high-modulation fringes. FMEM enables to calculate the desired phase from the transformed set of high-modulation fringes, and result in accurate 3-D FPP without sacrificing the speed. Experimental analysis verifies its effectiveness and accurateness. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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