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13 December 2020 Denoising wavefront sensor images with deep neural networks
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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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
B. Pou, E. Quiñones, D. Gratadour, and M. Martin "Denoising wavefront sensor images with deep neural networks", Proc. SPIE 11448, Adaptive Optics Systems VII, 114484J (13 December 2020); https://doi.org/10.1117/12.2576242
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KEYWORDS
Adaptive optics

Adaptive control

Telescopes

Wavefront sensors

Astronomy

Atmospheric turbulence

Deformable mirrors

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