@inproceedings{1d3f4e93b46449df816cddc569ea0e45,
title = "Denoising wavefront sensor images with deep neural networks",
abstract = "A classical closed-loop adaptive optics system with a Shack-Hartmann wavefront sensor (WFS) relies on a center of gravity approach to process the WFS information and an integrator with gain to produce the commands to a Deformable Mirror (DM) to compensate wavefront perturbations. In this kind of systems, noise in the WFS images can propagate to errors in centroids computation, and thus, lead the AO system to perform poorly in closed-loop operations. In this work, we present a deep supervised learning method to denoise the WFS images based on convolutional denoising autoencoders. Our method is able to denoise the images up to a high noise level and improve the integrator performance almost to the level of a noise-free situation.",
keywords = "Autoencoder, Machine Learning in AO, Noise",
author = "B. Pou and E. Qui{\~n}ones and D. Gratadour and M. Martin",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Adaptive Optics Systems VII 2020 ; Conference date: 14-12-2020 Through 22-12-2020",
year = "2020",
doi = "10.1117/12.2576242",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Laura Schreiber and Dirk Schmidt and Elise Vernet",
booktitle = "Adaptive Optics Systems VII",
address = "United States",
}