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...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 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 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 (Le FTP) module.By 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 methods.Guided 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 networks.Experimental results demonstrate that PI-FPA enables 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 approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.展开更多
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,h...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.展开更多
In fringe projection profilometry 3D measurement systems,the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras.The main solution involves using...In fringe projection profilometry 3D measurement systems,the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras.The main solution involves using multiple exposures to modulate fringe intensity;however,it is inefficient.In this study,we introduce an attention-guided end-to-end phase calculation network to accelerate the multi-exposure structured light process for high dynamic range(HDR)measurements.We use attention modules to guide feature selection,enhancing relevant features and suppressing irrelevant features.Using the 12-step phase-shifting profilometry(PSP)as ground truth,our method accurately extracts the sine and cosine components of the fundamental frequency from a single pattern to retrieve the absolute phases.Tested on our metallic dataset requiring HDR imaging,our method achieves an absolute phase error of 0.084,close to that of the six-step PSP method(0.069),while using only 16.7%of the time.On the ceramic dataset,our method achieves 0.021 phase error,close to that of the four-step PSP(0.012).In quantitative measurements,our method achieves an accuracy of approximately 40μm on standard spheres and plates.Overall,our method preserves the accuracy of multi-exposure PSP methods while significantly accelerating the 3D reconstruction process.展开更多
We introduce a variationalmethod for demodulating phasemaps fromfringe patterns.This newmethod is based on themean curvature of the level sets of the phase surface that is used for regularization.The performance of th...We introduce a variationalmethod for demodulating phasemaps fromfringe patterns.This newmethod is based on themean curvature of the level sets of the phase surface that is used for regularization.The performance of the method is illustrated with both synthetic and real data.展开更多
基金funded by National Key Research and Development Program of China (2022YFB2804603,2022YFB2804604)National Natural Science Foundation of China (62075096,62205147,U21B2033)+7 种基金China Postdoctoral Science Foundation (2023T160318,2022M711630,2022M721619)Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB254)The Leading Technology of Jiangsu Basic Research Plan (BK20192003)The“333 Engineering”Research Project of Jiangsu Province (BRA2016407)The Jiangsu Provincial“One belt and one road”innovation cooperation project (BZ2020007)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense (JSGP202105)Fundamental Research Funds for the Central Universities (30922010405,30921011208,30920032101,30919011222)National Major Scientific Instrument Development Project (62227818).
文摘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 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 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 (Le FTP) module.By 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 methods.Guided 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 networks.Experimental results demonstrate that PI-FPA enables 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 approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.
基金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)+2 种基金the Outstanding Youth Foundation of Jiangsu Province(BK20170034)the China Postdoctoral Science Foundation(2017M621747)the Jiangsu Planned Projects for Postdoctoral Research Funds(1701038A).
文摘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.
基金funded by Shenzhen Science and Technology Program(Grant JCYJ20240813112003005)by The Major Key Project of Pengcheng Laboratory(PCL2023A09).
文摘In fringe projection profilometry 3D measurement systems,the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras.The main solution involves using multiple exposures to modulate fringe intensity;however,it is inefficient.In this study,we introduce an attention-guided end-to-end phase calculation network to accelerate the multi-exposure structured light process for high dynamic range(HDR)measurements.We use attention modules to guide feature selection,enhancing relevant features and suppressing irrelevant features.Using the 12-step phase-shifting profilometry(PSP)as ground truth,our method accurately extracts the sine and cosine components of the fundamental frequency from a single pattern to retrieve the absolute phases.Tested on our metallic dataset requiring HDR imaging,our method achieves an absolute phase error of 0.084,close to that of the six-step PSP method(0.069),while using only 16.7%of the time.On the ceramic dataset,our method achieves 0.021 phase error,close to that of the four-step PSP(0.012).In quantitative measurements,our method achieves an accuracy of approximately 40μm on standard spheres and plates.Overall,our method preserves the accuracy of multi-exposure PSP methods while significantly accelerating the 3D reconstruction process.
文摘We introduce a variationalmethod for demodulating phasemaps fromfringe patterns.This newmethod is based on themean curvature of the level sets of the phase surface that is used for regularization.The performance of the method is illustrated with both synthetic and real data.
