Near-infrared fringe projection profilometry based on deep learning

Jinglei Wang; Yixuan Li; Mengke Wang; Yanxin Zhang; Zhe Jia; Yuzhen Zhang

doi:10.1117/12.2605009

3 December 2021 Near-infrared fringe projection profilometry based on deep learning

Jinglei Wang, Yixuan Li, Mengke Wang, Yanxin Zhang, Zhe Jia, Yuzhen Zhang

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 1206909 (2021) https://doi.org/10.1117/12.2605009
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Near-infrared (NIR) fringe projection is gradually replacing visible fringe projection in face-scanning because NIR light is less harmful to human eyes and has a higher recognition rate in a special environment. However, since NIR is susceptible to interference from various heat sources and light sources, the NIR fringe image captured by the camera is of poor quality and has low contrast. And the captured low-quality fringe image will directly affect the quality of phase acquisition. Traditional phase acquisition methods, such as Fourier transform profilometry and phase-shifting profilometry, are difficult to achieve both high-speed and high-precision phase measurements at the same time. Therefore, this paper proposes a deep learning based phase acquisition method for NIR fringe projection. By using a deep learning model trained by the deep neural network with powerful learning and computing capabilities, phase extraction can be achieved from fewer NIR fringe images. Moreover, our method can retrieve the phase information with high speed and high quality without additional optimization of the original fringe map.

Citation Download Citation

Jinglei Wang, Yixuan Li, Mengke Wang, Yanxin Zhang, Zhe Jia, and Yuzhen Zhang "Near-infrared fringe projection profilometry based on deep learning", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 1206909 (3 December 2021); https://doi.org/10.1117/12.2605009

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KEYWORDS

Fringe analysis

Near infrared

Phase shifts

Denoising

Optical filters

Cameras

3D metrology

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