Articles | Volume 9, issue 2
https://doi.org/10.5194/jsss-9-301-2020
https://doi.org/10.5194/jsss-9-301-2020
Regular research article
 | 
24 Sep 2020
Regular research article |  | 24 Sep 2020

Deep neural networks for computational optical form measurements

Lara Hoffmann and Clemens Elster

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Cited articles

Baer, G., Schindler, J., Pruss, C., and Osten, W.: Correction of misalignment introduced aberration in non-null test measurements of free-form surfaces, JEOS:RP, 8, 13074, https://doi.org/10.2971/jeos.2013.13074, 2013. a
Baer, G., Schindler, J., Pruss, C., Siepmann, J., and Osten, W.: Calibration of a non-null test interferometer for the measurement of aspheres and free-form surfaces, Opt. Express, 22, 31200–31211, https://doi.org/10.1364/OE.22.031200, 2014. a
Barbastathis, G., Ozcan, A., and Situ, G.: On the use of deep learning for computational imaging, Optica, 6, 921–943, https://doi.org/10.1364/OPTICA.6.000921, 2019. a
Chung, B.-M.: Neural-Network Model for Compensation of Lens Distortion in Camera Calibration, Int. J. Precis. Eng. Man., 19, 959–966, https://doi.org/10.1007/s12541-018-0113-0, 2018. a
de Bézenac, E., Pajot, A., and Gallinari, P.: Deep learning for physical processes: incorporating prior scientific knowledge, J. Stat. Mech.-Theory E., 2019, 124009, https://doi.org/10.1088/1742-5468/ab3195, 2019. a
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Short summary
Deep learning has become a state-of-the-art method in machine learning, with a broad range of successful applications. Our goal is to explore the benefits of deep learning techniques for computational optical form measurements. The research is based on solving a nonlinear inverse problem aimed at the reconstruction of optical topographies from given processed interferograms. A U-Net network structure is chosen and tested on a simulated database. The obtained results are promising.