B. Pou,1,2 E. Quiñones,1 D. Gratadour,3,4 M. Martin2
1Barcelona Supercomputing Ctr. - Ctr. Nacional de Supercomputación (Spain) 2Univ. Politècnica de Catalunya (Spain) 3The Australian National Univ. (Australia) 4Lab. d'Etudes Spatiales et d'Instrumentation en Astrophysique (France)
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.
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.
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.
The alert did not successfully save. Please try again later.
B. Pou, E. Quiñones, D. Gratadour, 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