首页|Physics-informed deep learning for fringe pattern analysis

Physics-informed deep learning for fringe pattern analysis

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Recently,deep learning has yielded transformative success across optics and photonics,especially in optical metrology.Deep neural networks(DNNs)with a fully convolutional architecture(e.g.,U-Net and its derivatives)have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks,such as fringe denoising,phase unwrapping,and fringe analysis.However,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naïve,as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice.To this end,we introduce a physics-informed deep learning method for fringe pattern analysis(PI-FPA)to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry(LeFTP)module.By para-meterizing conventional phase retrieval methods,the LeFTP module embeds the prior knowledge in the network struc-ture and the loss function to directly provide reliable phase results for new types of samples,while circumventing the re-quirement of collecting a large amount of high-quality data in supervised learning methods.Guided by the initial phase from LeFTP,the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks.Experimental results demonstrate that PI-FPA en-ables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during training.The proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning ap-proaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.

optical metrologydeep learningphysics-informed neural networksfringe analysisphase retrieval

Wei Yin、Yuxuan Che、Xinsheng Li、Mingyu Li、Yan Hu、Shijie Feng、Edmund Y.Lam、Qian Chen、Chao Zuo

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Smart Computational Imaging Laboratory(SCILab),School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China

Smart Computational Imaging Research Institute(SCIRI)of Nanjing University of Science and Technology,Nanjing 210019,China

Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense,Nanjing 210094,China

Department of Electrical and Electronic Engineering,The University of Hong Kong,Pokfulam,Hong Kong SAR 999077,China

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National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Natural science Foundation of ChinaNational Natural science Foundation of ChinaNational Natural science Foundation of ChinaChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationJiangsu Funding Program for Excellent Postdoctoral TalentLeading Technology of Jiangsu Basic Research PlanThe"333 Engineering"Research Project of Jiangsu ProvinceJiangsu Provincial"One belt and one road"innovation cooperation projectOpen Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent SenseFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central UniversitiesNational Major Scientific Instrument Development Project

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2024

10.29026/oea.2024.230034

光电进展(英文版)

光电进展(英文版)

EI
ISSN：
年,卷(期)：2024.7(1)
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