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31 July 2023 UNet++ for estimating physical parameters from Newton’s rings
Saihui Fan, Mingfeng Lu, Xiaoxin Xiong, Jinmin Wu, Deming Shen, Ran Tao
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Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127470S (2023) https://doi.org/10.1117/12.2689318
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China
Abstract
Newton’s rings pattern is frequently encountered in optical interferometry, and by extracting the phase contained in it, the measured physical parameter information can be obtained. According to the purpose of point-to-point mapping of the image to be analyzed, a method based on UNet++ to extract the phase of Newton’s rings is proposed. Once the network training is completed, the continuous phase including the curvature radius and ring’s center coordinate can be directly predicted from a single Newton’s rings pattern immediately. The relative error of the curvature radius obtained by parameter fitting the phase is less than 0.83%, and the error of ring’s center coordinate is close to 0 pixel. In order to further improve the results of curvature radius estimation, the parameter estimation results obtained by UNet++ are taken as the initial value and corrected by the least-squares fitting method. Experimental results show that for the Newton’s rings pattern containing -5 dB Gaussian noise, the relative error of the corrected curvature radius is no higher than 0.31%.
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Saihui Fan, Mingfeng Lu, Xiaoxin Xiong, Jinmin Wu, Deming Shen, and Ran Tao "UNet++ for estimating physical parameters from Newton’s rings", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127470S (31 July 2023); https://doi.org/10.1117/12.2689318
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KEYWORDS
Error analysis
Signal to noise ratio
Phase distribution
Fringe analysis
Spherical lenses
Machine learning
Neural networks
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