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Fully Convolutional Approaches for Numerical Approximation of Turbulent Phases in Solar Adaptive Optics

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  • Francisco García Riesgo

    (Department of Physics, University of Oviedo, 33007 Oviedo, Spain
    Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain)

  • Sergio Luis Suárez Gómez

    (Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain
    Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain)

  • Enrique Díez Alonso

    (Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain
    Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain)

  • Carlos González-Gutiérrez

    (Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain
    Computer Sciences Department, University of Oviedo, 33024 Gijón, Spain)

  • Jesús Daniel Santos

    (Department of Physics, University of Oviedo, 33007 Oviedo, Spain
    Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), 33004 Oviedo, Spain)

Abstract

Information on the correlations from solar Shack–Hartmann wavefront sensors is usually used for reconstruction algorithms. However, modern applications of artificial neural networks as adaptive optics reconstruction algorithms allow the use of the full image as an input to the system intended for estimating a correction, avoiding approximations and a loss of information, and obtaining numerical values of those correlations. Although studied for night-time adaptive optics, the solar scenario implies more complexity due to the resolution of the solar images potentially taken. Fully convolutional neural networks were the technique chosen in this research to address this problem. In this work, wavefront phase recovery for adaptive optics correction is addressed, comparing networks that use images from the sensor or images from the correlations as inputs. As a result, this research shows improvements in performance for phase recovery with the image-to-phase approach. For recovering the turbulence of high-altitude layers, up to 93% similarity is reached.

Suggested Citation

  • Francisco García Riesgo & Sergio Luis Suárez Gómez & Enrique Díez Alonso & Carlos González-Gutiérrez & Jesús Daniel Santos, 2021. "Fully Convolutional Approaches for Numerical Approximation of Turbulent Phases in Solar Adaptive Optics," Mathematics, MDPI, vol. 9(14), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1630-:d:591899
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    References listed on IDEAS

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    1. Yurii Nesterov, 2018. "Lectures on Convex Optimization," Springer Optimization and Its Applications, Springer, edition 2, number 978-3-319-91578-4, September.
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