低信噪比场景的光学三维测量——基于物理的零射击学习

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Fuqian Li;Qican Zhang;Yajun Wang
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引用次数: 0

摘要

在工业三维测量中,由于光照强度不合适、成像动态范围有限或场景材料复杂等原因,通常会遇到低信噪比(SNR)问题。与非学习方法相比,基于深度学习的方法在低信噪比问题上具有更高的效率和保真度。然而,它们大多是数据驱动的,因此泛化能力有限。此外,它们需要先进的计算硬件进行网络训练,大大增加了计量成本。为了解决这些问题,提出了一种基于超轻量级神经网络(UNN)的基于物理信息的零射击学习(PZL)方法,用于低信噪比场景测量。我们的方法有两个主要贡献。首先,通过混合相位恢复的物理先验和条纹噪声,建立了具有噪声-正弦分量到噪声-正弦分量(NS2NS)映射的广义PZL框架。将低照度、高动态范围、强环境光、大景深范围等各种具有挑战性的场景的低信噪比问题统一到一个增强框架中。此外,除了退化的条纹本身外,不需要任何训练数据集,显著提高了条纹增强的泛化能力。其次,在PZL框架的基础上,提出了一种基于UNN的对称优化策略。在计算资源受限的平台上,甚至在CPU上,都可以实现精细表面细节的有效三维重建。实验验证了该方法在效率、保真度、泛化能力和计算硬件成本等方面的优越性。据我们所知,这是第一次同时取得这样的成就。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical 3-D Measurement for Low-SNR Scenes via Physics-Informed Zero-Shot Learning
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.
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
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.
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