Improved training for the deep learning wavefront sensor

Camilo Weinberger; Felipe Guzmán; Esteban Vera

doi:10.1117/12.2564199

13 December 2020 Improved training for the deep learning wavefront sensor

Camilo Weinberger, Felipe Guzmán, Esteban Vera

Proceedings Volume 11448, Adaptive Optics Systems VII; 114484G (2020) https://doi.org/10.1117/12.2564199
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

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.

Citation Download Citation

Camilo Weinberger, Felipe Guzmán, and Esteban Vera "Improved training for the deep learning wavefront sensor", Proc. SPIE 11448, Adaptive Optics Systems VII, 114484G (13 December 2020); https://doi.org/10.1117/12.2564199

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available