{"title":"Optical 3-D Measurement for Low-SNR Scenes via Physics-Informed Zero-Shot Learning","authors":"Fuqian Li;Qican Zhang;Yajun Wang","doi":"10.1109/TIM.2025.3606025","DOIUrl":null,"url":null,"abstract":"In industrial 3-D metrology, the low signal-to-noise ratio (SNR) issue is commonly encountered, due to inappropriate illumination intensity, limited imaging dynamic range, or complex scene material, etc. Compared with nonlearning-based methods, deep-learning-based methods excel in efficiency and fidelity for the low SNR issue. However, most of them are data-driven, thus have limited generalization ability. Besides, they require advanced computing hardware for network training, greatly increasing the metrology cost. To tackle these problems, a physics-informed zero-shot learning (PZL) method with an ultralightweight neural network (UNN) is proposed for low-SNR scene measurement. There are two major contributions in our method. First, by blending physics priors for phase retrieval and fringe noise, a generalized PZL framework with a noisy-sinusoidal-component-to-noisy-sinusoidal-component (NS2NS) mapping is established. The low SNR issue of various challenging scenes including the low-illumination, high-dynamic-range, strong-ambient-light, and large-depth-range scenes is unified in a single enhancement framework. Moreover, no training dataset is required other than the degraded fringe itself, and the generalization ability for fringe enhancement is significantly improved. Second, based on the PZL framework, a symmetrized optimization strategy along with the UNN is proposed. Valid 3-D reconstruction of fine surface details can be achieved on computing-resource-constrained platforms, even on a CPU. Experiments verify the superiority of our method in efficiency, fidelity, generalization ability, and computing hardware cost. And to our knowledge, it is the first time such a simultaneous achievement has been accomplished.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11151597/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In industrial 3-D metrology, the low signal-to-noise ratio (SNR) issue is commonly encountered, due to inappropriate illumination intensity, limited imaging dynamic range, or complex scene material, etc. Compared with nonlearning-based methods, deep-learning-based methods excel in efficiency and fidelity for the low SNR issue. However, most of them are data-driven, thus have limited generalization ability. Besides, they require advanced computing hardware for network training, greatly increasing the metrology cost. To tackle these problems, a physics-informed zero-shot learning (PZL) method with an ultralightweight neural network (UNN) is proposed for low-SNR scene measurement. There are two major contributions in our method. First, by blending physics priors for phase retrieval and fringe noise, a generalized PZL framework with a noisy-sinusoidal-component-to-noisy-sinusoidal-component (NS2NS) mapping is established. The low SNR issue of various challenging scenes including the low-illumination, high-dynamic-range, strong-ambient-light, and large-depth-range scenes is unified in a single enhancement framework. Moreover, no training dataset is required other than the degraded fringe itself, and the generalization ability for fringe enhancement is significantly improved. Second, based on the PZL framework, a symmetrized optimization strategy along with the UNN is proposed. Valid 3-D reconstruction of fine surface details can be achieved on computing-resource-constrained platforms, even on a CPU. Experiments verify the superiority of our method in efficiency, fidelity, generalization ability, and computing hardware cost. And to our knowledge, it is the first time such a simultaneous achievement has been accomplished.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.