Aliasing fringe pattern denoising based on deep learning

Xiaoxi W.; Yingjie Y.; Jianbin H.

doi:10.1117/12.2606566

24 November 2021 Aliasing fringe pattern denoising based on deep learning

Xiaoxi W., Yingjie Y., Jianbin H.

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 120690S (2021) https://doi.org/10.1117/12.2606566
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Wavelength tuning laser interferometry can measure the front and rear surface profile and thickness variation of transparent plate at one time. Separating the collected aliasing fringe patterns containing multi-surface interference information can obtain the surface shape information of each surface of the transparent plate. However, in the process of image acquisition and transmission, it will inevitably be affected by noise, and the existence of noise will affect the separation of multi surface shape information, and further affect the recovery of each surface phase and the accurate acquisition of three-dimensional shape. In this paper, a noise reduction method of aliasing fringe pattern based on convolutional neural network is proposed. The simulation data and experimental fringe patterns show that the network can effectively improve the quality of fringe patterns, has faster calculation speed.

Citation Download Citation

Xiaoxi W., Yingjie Y., and Jianbin H. "Aliasing fringe pattern denoising based on deep learning", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 120690S (24 November 2021); https://doi.org/10.1117/12.2606566

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