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Analysis of the wavefront aberrations based on neural networks processing of the interferograms with a conical reference beam

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Abstract

Recently, intelligent data analysis and neural networks are increasingly used to detect the wavefront by interferograms and digital holograms. In this case, there is significant freedom to choose the structure of the reference beam. In this paper, a comparative study of the effectiveness of the neural network was performed to solve the problem of the wavefront aberration recognition based on off-axis and inline schemes of digital holography with a plane and conical reference wavefront, respectively. The feature of the inline digital holograms with the conical wavefront compared to the off-axis is the invariance of their structure to the rotation of the wavefront at some angle. In addition, a numerical analysis of the sensitivity of the types of digital holograms under consideration to changes in the wavefront for a different level of aberration showed relatively better characteristics for the average level of aberration, when it is difficult to apply detecting methods based on the Shack-Hartmann sensors or matched filtering. These features of inline digital holograms with a conical reference wavefront made it possible to increase the recognition efficiency for types and levels of aberrations using neural networks. As a result, the average absolute recognition error for model interferogram decreases more than three times. The results for the experimental conical and linear interferograms turned out to be quite close because of the sensitivity of the conical wavefront to the alignment of the optical system. Moreover, the neural network trained on a more diverse experimental data set, which contains natural distortions of image registration, gives an increase in the average recognition accuracy for linear-type interferograms. Thus, in the future, it is reasonable to consider the combined use of various types of interferograms.

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Acknowledgements

This work was supported by the Russian Foundation for Basic Research under Grant No. 20-37-90129 (numerical modeling) and under Grant No. 19-29-09054 (data analysis), by the Ministry of Science and Higher Education of the Russian Federation under a government project of the Federal Research Center "Crystallography and Photonics" RAS No. 007-GZ/Ch3363/26 (theoretical research), by President of Russian Federation Grant No. MD-6101.2021.1.2 (experimental results).

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Authors and Affiliations

  1. IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, Russia

    S. N. Khonina & P. G. Serafimovich

  2. Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia

    S. N. Khonina & P. A. Khorin

  3. ITMO University, Kronverkskiy 49, 197101, Saint-Petersburg, Russia

    A. P. Dzyuba, A. O. Georgieva & N. V. Petrov

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  1. S. N. Khonina
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  2. P. A. Khorin
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  3. P. G. Serafimovich
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  4. A. P. Dzyuba
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  5. A. O. Georgieva
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  6. N. V. Petrov
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Correspondence to S. N. Khonina or N. V. Petrov.

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Khonina, S.N., Khorin, P.A., Serafimovich, P.G. et al. Analysis of the wavefront aberrations based on neural networks processing of the interferograms with a conical reference beam. Appl. Phys. B 128, 60 (2022). https://doi.org/10.1007/s00340-022-07778-y

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