Mr. Yuxuan Che Profile

Yuxuan Che

at Nanjing Univ Of Science And Technology

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Proceedings Article | 18 December 2023 Paper

A lightweight fringe analysis network based on deep learning

Yuxuan Che, Wei Yin

Proceedings Volume 12967, 1296709 (2023) https://doi.org/10.1117/12.3007623

KEYWORDS: Fringe analysis, Deep learning, Optical metrology, Phase retrieval, Fourier transforms, Picosecond phenomena, Education and training, Tunable filters, Phase recovery, Network security

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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.

Proceedings Article | 21 December 2022 Poster

Fast 3D measurement based on fringe projection profilometry and deep learning

Yuxuan Che, Wei Yin

Proceedings Volume 12319, 1231915 (2022) https://doi.org/10.1117/12.2642326

KEYWORDS: Fringe analysis, Network security, Optical metrology, Phase retrieval, Mobile devices, Holograms

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Deep learning is currently gaining a lot of attention in the field of optical metrology and has shown great potential in solving various optical metrology tasks such as fringe analysis, phase unwrapping, and hologram reconstruction. For fringe analysis, current major works use U-Net and its derivatives as the backbone of the deep learning network, but suffer from a large number of model parameters and computational redundancy of the U-Net network, which outputs low-precision prediction results while taking up a lot of GPU memory. To solve these problems, compared with U-Net, a lightweight fringe analysis network with the size of only 1.7G is proposed to reduce the memory usage by over 70%, while improving the accuracy of phase retrieval by 10%, providing a new path for the widespread implementation in mobile devices of deep learning-based optical metrology.

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