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Physics-informed deep learning for fringe pattern analysis

Opto-Electronic Advances 2024年第4期7卷 4-15页

作者： Wei Yin Yuxuan Che Xinsheng Li Mingyu Li Yan Hu Shijie Feng Edmund Y.Lam Qian Chen Chao Zuo Smart Computational Imaging Laboratory(SCILab) School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjing 210094China Smart Computational Imaging Research Institute(SCIRI)of Nanjing University of Science and Technology Nanjing 210019China Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense Nanjing 210094China Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong Kong SAR 999077China

Recently,deep learning has yielded transformative success across optics and photonics,especially in optical *** 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 ***,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naive,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 *** 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 (Le FTP) *** parameterizing conventional phase retrieval methods,the Le FTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples,while circumventing the requirement of collecting a large amount of high-quality data in supervised learning *** by the initial phase from Le FTP,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 *** results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during *** 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 approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.

关键词： optical metrology deep learning physics-informed neural networks fringe analysis phase retrieval

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fringe pattern analysis using deep learning

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Advanced Photonics 2019年第2期1卷 34-40页

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

In many optical metrology techniques,fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe *** 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 *** 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 *** effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection *** 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.

关键词： fringe analysis phase measurement deep learning

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Modified local mean decomposition algorithm for adaptive analysis of fringe pattern

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Chinese Optics Letters 2014年第A01期12卷 62-65页

作者：王辰星达飞鹏陆可 School of Automation Southeast UniversityNanjing 210096China School of Engineering The University of EdinburghThe King's Buildings CampusWest Main RoadEdinburghUK

We propose modified local mean decomposition to analyze signals of fringe pattern adaptively. It decomposes the signals into a set of functions, each of which is the product of amplitude signal and frequency signal. Then, the physical components of noise and background are extracted according to the corresponding product functions. Moreover, to solve the most likely mode mixing problem, a high-frequency signal is constructed according to the amplitude and frequency characteristics of the intrinsic noise. Using the presented method, carrier signals are recovered accurately as well as the wrapped phase. Compared experiments illustrate the validity of this method.

关键词： 100.2650 fringe analysis 100.5070 Phase retrieval 120.2650 fringe analysis

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A study of fringe pattern trace and analysis

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Chinese Journal of Lasers 1992年第2期 83-88页

作者： Guiying Wang, Aiming Sun, Jun Ren, Zhijiang Wang(Shanghai Institute of Optics and Fine Mechanics, Academia Sinica, P. 0. Box 800-211, Shanghai 201800, PRC)

In this paper,the gometric center and centroid method are suggested to tracecenter location of a close fringe *** fringe pattern of the interferogram is analysed byusing the general Fourier *** analytic accuracy is controlled by using P-test methodof *** analytic accuracy and sampling speed of the fringe pattern depend on thequality of the interferogram and the numbers of the sampling points.

关键词： fringe analysis centroid method geometric center method F-test method

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A Mean Curvature Regularized Based Model for Demodulating Phase Maps from fringe Patterns

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Communications in Computational Physics 2018年第6期24卷 27-43页

作者： Carlos Brito-Loeza Ricardo Legarda-Saenz Arturo Espinosa-Romero Anabel Martin-Gonzalez CLIR at Facultad de Matematicas Universidad Autonoma de YucatanMexico

We introduce a variationalmethod for demodulating phasemaps fromfringe *** newmethod is based on themean curvature of the level sets of the phase surface that is used for *** performance of the method is illustrated w... 详细信息

关键词： fringe analysis interferometry high-order regularization variational models

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