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Recognition of wavefront aberrations types corresponding to single Zernike functions from the pattern of the point spread function in the focal plane using neural networks
I.A. Rodin 1, S.N. Khonina 1,2, P.G. Serafimovich 2, S.B. Popov 1,2

1 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
2 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 1363 kB

DOI: 10.18287/2412-6179-CO-810

Pages: 923-930.

Full text of article: Russian language.

Abstract:
In this work, we carried out training and recognition of the types of aberrations corresponding to single Zernike functions, based on the intensity pattern of the point spread function (PSF) using convolutional neural networks. PSF intensity patterns in the focal plane were modeled using a fast Fourier transform algorithm. When training a neural network, the learning coefficient and the number of epochs for a dataset of a given size were selected empirically. The average prediction errors of the neural network for each type of aberration were obtained for a set of 15 Zernike functions from a data set of 15 thousand PSF pictures. As a result of training, for most types of aberrations, averaged absolute errors were obtained in the range of 0.012 – 0.015. However, determining the aberration coefficient (magnitude) requires additional research and data, for example, calculating the PSF in the extrafocal plane.

Keywords:
wavefront aberrations, point spread function, focal plane, fast Fourier transform, neural networks.

Citation:
Rodin IA, Khonina SN, Serafimovich PG, Popov SB. Recognition of wavefront aberrations types corresponding to single Zernike functions from the pattern of the point spread function in the focal plane using neural networks. Computer Optics 2020; 44(6): 923-930. DOI: 10.18287/2412-6179-CO-810.

Acknowledgements:
The study was carried out with the financial support of the RFBR in the framework of the scientific project No. 19-29-09054 in terms of machine learning and neural networks, as well as the Ministry of Science and Higher Education of the Russian Federation in within the framework of work under the State task of the Federal Research Center "Crystallography and Photonics" RAS (agreement No. 007-GZ / Ch3363 / 26) parts of aberrated wavefront modeling and PSF calculation.

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