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Defocused Image Deep Learning Designed for Wavefront Reconstruction in Tomographic Pupil Image Sensors

Author

Listed:
  • Sergio Luis Suárez Gómez

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

  • Francisco García Riesgo

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

  • Carlos González Gutiérrez

    (Department of Physics, University of Oviedo, 33007 Oviedo, Spain
    Computer Sciences Department, University of Oviedo, 33024 Gijón, Spain)

  • Luis Fernando Rodríguez Ramos

    (Instituto de Astrofísica de Canarias, 38205 San Cristóbal de La Laguna, Spain)

  • Jesús Daniel Santos

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

Abstract

Mathematical modelling methods have several limitations when addressing complex physics whose calculations require considerable amount of time. This is the case of adaptive optics, a series of techniques used to process and improve the resolution of astronomical images acquired from ground-based telescopes due to the aberrations introduced by the atmosphere. Usually, with adaptive optics the wavefront is measured with sensors and then reconstructed and corrected by means of a deformable mirror. An improvement in the reconstruction of the wavefront is presented in this work, using convolutional neural networks (CNN) for data obtained from the Tomographic Pupil Image Wavefront Sensor (TPI-WFS). The TPI-WFS is a modified curvature sensor, designed for measuring atmospheric turbulences with defocused wavefront images. CNNs are well-known techniques for its capacity to model and predict complex systems. The results obtained from the presented reconstructor, named Convolutional Neural Networks in Defocused Pupil Images (CRONOS), are compared with the results of Wave-Front Reconstruction (WFR) software, initially developed for the TPI-WFS measurements, based on the least-squares fit. The performance of both reconstruction techniques is tested for 153 Zernike modes and with simulated noise. In general, CRONOS showed better performance than the reconstruction from WFR in most of the turbulent profiles, with significant improvements found for the most turbulent profiles; overall, obtaining around 7% of improvements in wavefront restoration, and 18% of improvements in Strehl.

Suggested Citation

  • Sergio Luis Suárez Gómez & Francisco García Riesgo & Carlos González Gutiérrez & Luis Fernando Rodríguez Ramos & Jesús Daniel Santos, 2020. "Defocused Image Deep Learning Designed for Wavefront Reconstruction in Tomographic Pupil Image Sensors," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2020:i:1:p:15-:d:466996
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    References listed on IDEAS

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    1. Javier DE ANDRES & Fernando SÁNCHEZ-LASHERAS & Pedro LORCA & Francisco Javier DE COS JUEZ, 2011. "A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 10(3), pages 351-374, September.
    2. Carlos González-Gutiérrez & María Luisa Sánchez-Rodríguez & José Luis Calvo-Rolle & Francisco Javier de Cos Juez, 2018. "Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics," Complexity, Hindawi, vol. 2018, pages 1-9, March.
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