Physics-informed deep learning for fringe pattern analysis
물리 기반 에지 모드 분석 딥러닝
Aprendizaje profundo del análisis de patrones de borde basado en la física
Deep Learning basé sur la physique pour l'analyse des modèles Edge
Глубокое обучение на основе физического анализа периферийных моделей
Wei Yin 尹维 ¹ ² ³, Yuxuan Che 车宇轩 ¹ ² ³, Xinsheng Li 李鑫胜 ¹ ² ³, Mingyu Li 李明雨 ¹ ² ³, Yan Hu 胡岩 ¹ ² ³, Shijie Feng 冯世杰 ¹ ² ³, Edmund Y. Lam 林彥民 ⁴, Qian Chen 陈钱 ³, Chao Zuo 左超 ¹ ² ³
¹ Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
中国 南京 南京理工大学光电信息科学与工程学院 智能计算成像实验室
² Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210019, China
中国 南京 南京理工大学智能计算成像研究院
³ Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing 210094, China
中国 南京 江苏省光谱成像与智能感知重点实验室
⁴ Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR 999077, China
中国 香港 薄扶林 香港大学电气电子工程系
Opto-Electronic Advances, 25 January 2024

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 parameterizing conventional phase retrieval methods, the LeFTP 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 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 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.
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