Improved training for the deep learning wavefront sensor

Camilo Weinberger, Felipe Guzmán, ESTEBAN MAURICIO VERA ROJAS

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We have recently proposed the deep learning wavefront sensor, capable of directly estimating Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. However, deep neural networks demand an intensive training stage, where more training examples allow to improve the accuracy and increase the amount of the estimated Zernike modes. Since low order aberrations such as tip and tilt only produce space-invariant motion of the PSF, we propose to treat tip and tilt estimation separately when training the deep learning wavefront sensor, decreasing the training efforts while keeping the wavefront sensor performance. In this paper, we also introduce and test simpler architectures for deep learning wavefront sensing, while exploring the impact of reducing the number of pixels to estimate a given amount of Zernike coefficients. Our preliminary results indicate that we can achieve a significant prediction speedup aiming for real time adaptive optics systems.

Original languageEnglish
Title of host publicationAdaptive Optics Systems VII
EditorsLaura Schreiber, Dirk Schmidt, Elise Vernet
PublisherSPIE
ISBN (Electronic)9781510636835
DOIs
StatePublished - 2020
Externally publishedYes
EventAdaptive Optics Systems VII 2020 - Virtual, Online, United States
Duration: 14 Dec 202022 Dec 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11448
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAdaptive Optics Systems VII 2020
CountryUnited States
CityVirtual, Online
Period14/12/2022/12/20

Keywords

  • Adaptive optics
  • Machine-learning
  • Wavefront sensor

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