A lightweight fringe analysis network based on deep learning

Yuxuan Che; Wei Yin

doi:10.1117/12.3007623

18 December 2023 A lightweight fringe analysis network based on deep learning

Yuxuan Che, Wei Yin

Proceedings Volume 12967, AOPC 2023: Computing Imaging Technology; 1296709 (2023) https://doi.org/10.1117/12.3007623
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

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 na¨ıve, 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 PI-FPA enables more accurate and computationally efficient single-shot phase retrieval.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuxuan Che and Wei Yin "A lightweight fringe analysis network based on deep learning", Proc. SPIE 12967, AOPC 2023: Computing Imaging Technology, 1296709 (18 December 2023); https://doi.org/10.1117/12.3007623

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KEYWORDS

Fringe analysis

Deep learning

Optical metrology

Phase retrieval

Fourier transforms

Education and training

Picosecond phenomena

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