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首页 > 论文 > Advanced Photonics > 1卷 > 2期(pp:25001--1)

Fringe pattern analysis using deep learning

Fringe pattern analysis using deep learning

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Abstract

In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance, in terms of high accuracy and edge-preserving, over two representative single-frame techniques: Fourier transform profilometry and windowed Fourier transform profilometry.

Newport宣传-MKS新实验室计划
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DOI:10.1117/1.ap.1.2.025001

所属栏目:Letters

基金项目:This work was financially supported by the National Natural Science Foundation of China (61722506, 61705105, and 11574152), the National Key R&D Program of China (2017YFF0106403), the Outstanding Youth Foundation of Jiangsu Province (BK20170034), the China Postdoctoral Science Foundation (2017M621747), and the Jiangsu Planned Projects for Postdoctoral Research Funds (1701038A).

收稿日期:2018-08-22

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Shijie Feng:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, ChinaNanjing University of Science and Technology, Smart Computational Imaging Laboratory (SCILab), Nanjing, China
Qian Chen:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, China
Guohua Gu:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, China
Tianyang Tao:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, China
Liang Zhang:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, ChinaNanjing University of Science and Technology, Smart Computational Imaging Laboratory (SCILab), Nanjing, China
Yan Hu:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, ChinaNanjing University of Science and Technology, Smart Computational Imaging Laboratory (SCILab), Nanjing, China
Wei Yin:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, ChinaNanjing University of Science and Technology, Smart Computational Imaging Laboratory (SCILab), Nanjing, China
Chao Zuo:Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, ChinaNanjing University of Science and Technology, Smart Computational Imaging Laboratory (SCILab), Nanjing, China

联系人作者:Qian Chen([email protected])

【1】T.Kreis, Handbook of Holographic Interferometry: Optical and Digital Methods, John Wiley & Sons, Hoboken, New Jersey (2006).

【2】P. K.Rastogi, Digital Speckle Pattern Interferometry & Related Techniques, John Wiley & Sons, Hoboken, New Jersey (2000).

【3】S. S.Gorthi and P.Rastogi, “Fringe projection techniques: whither we are?,” Opt. Lasers Eng.48(2), 133–140 (2010).

【4】C.Zuoet al., “Phase shifting algorithms for fringe projection profilometry: a review,” Opt. Lasers Eng.109, 23–59 (2018).

【5】X.Su and Q.Zhang, “Dynamic 3-D shape measurement method: a review,” Opt. Lasers Eng.48(2), 191–204 (2010).

【6】Q.Kemao, “Two-dimensional windowed Fourier transform for fringe pattern analysis: principles, applications and implementations,” Opt. Lasers Eng.45(2), 304–317 (2007).

【7】J.Zhong and J.Weng, “Spatial carrier-fringe pattern analysis by means of wavelet transform: wavelet transform profilometry,” Appl. Opt.43(26), 4993–4998 (2004).0003-6935

【8】L.Huanget al., “Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase extraction from a single fringe pattern in fringe projection profilometry,” Opt. Lasers Eng.48(2), 141–148 (2010).

【9】Z.Zhanget al., “Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase calculation at discontinuities in fringe projection profilometry,” Opt. Lasers Eng.50(8), 1152–1160 (2012).

【10】A.Sinhaet al., “Lensless computational imaging through deep learning,” Optica4(9), 1117–1125 (2017).

【11】Y.Rivensonet al., “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl.7, 17141 (2018).

【12】C.Zuoet al., “Temporal phase unwrapping algorithms for fringe projection profilometry: a comparative review,” Opt. Lasers Eng.85, 84–103 (2016).

【13】C.Zuoet al., “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express20(17), 19493–19510 (2012).1094-4087

引用该论文

Shijie Feng,Qian Chen,Guohua Gu,Tianyang Tao,Liang Zhang,Yan Hu,Wei Yin,Chao Zuo. Fringe pattern analysis using deep learning[J]. Advanced Photonics, 2019, 1(2): 025001

Shijie Feng,Qian Chen,Guohua Gu,Tianyang Tao,Liang Zhang,Yan Hu,Wei Yin,Chao Zuo. Fringe pattern analysis using deep learning[J]. Advanced Photonics, 2019, 1(2): 025001

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