DanceCam: atmospheric turbulence mitigation in wide-field astronomical images with short-exposure video streams
BERTIN, Emmanuel
Institut Polytechnique de Paris [IP Paris]
Département Réseaux et Services Multimédia Mobiles [TSP - RS2M]
Network Systems and Services [NeSS-SAMOVAR]
Orange Innovation
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Institut Polytechnique de Paris [IP Paris]
Département Réseaux et Services Multimédia Mobiles [TSP - RS2M]
Network Systems and Services [NeSS-SAMOVAR]
Orange Innovation
BERTIN, Emmanuel
Institut Polytechnique de Paris [IP Paris]
Département Réseaux et Services Multimédia Mobiles [TSP - RS2M]
Network Systems and Services [NeSS-SAMOVAR]
Orange Innovation
Institut Polytechnique de Paris [IP Paris]
Département Réseaux et Services Multimédia Mobiles [TSP - RS2M]
Network Systems and Services [NeSS-SAMOVAR]
Orange Innovation
RIVET, Jean-Pierre
Joseph Louis LAGRANGE [LAGRANGE]
Université Côte d'Azur [UniCA]
Centre National de la Recherche Scientifique [CNRS]
Observatoire de la Côte d'Azur
Joseph Louis LAGRANGE [LAGRANGE]
Université Côte d'Azur [UniCA]
Centre National de la Recherche Scientifique [CNRS]
Observatoire de la Côte d'Azur
LAI, Olivier
Université Côte d'Azur [UniCA]
Centre National de la Recherche Scientifique [CNRS]
Observatoire de la Côte d'Azur
Joseph Louis LAGRANGE [LAGRANGE]
Université Côte d'Azur [UniCA]
Centre National de la Recherche Scientifique [CNRS]
Observatoire de la Côte d'Azur
Joseph Louis LAGRANGE [LAGRANGE]
CUILLANDRE, Jean-Charles
Astrophysique Interprétation Modélisation [AIM (UMR_7158 / UMR_E_9005 / UM_112)]
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Astrophysique Interprétation Modélisation [AIM (UMR_7158 / UMR_E_9005 / UM_112)]
Language
en
Article de revue
This item was published in
Monthly Notices of the Royal Astronomical Society. 2024-04-19, vol. 531, n° 1, p. 403-421
Oxford University Press (OUP): Policy P - Oxford Open Option A
English Abstract
ABSTRACT We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding ...Read more >
ABSTRACT We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure (∼0.2 s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image.Read less <
English Keywords
Methods
Data analysis
Observational
Image processing
Software
Simulations
Turbulence
Atmospheric effects
Origin
Hal imported