Astronomical Images Quality Assessment with Automated Machine Learning

Olivier Parisot; Pierrick Bruneau; Patrik Hitzelberger

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Paper

Astronomical Images Quality Assessment with Automated Machine Learning

Topics: Deep Learning; Information Quality

In Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1, 279-286, 2023 , Rome, Italy

Authors: Olivier Parisot ; Pierrick Bruneau and Patrik Hitzelberger

Affiliation: Luxembourg Institute of Science and Technology (LIST), 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg

Keyword(s): Astronomical Images, Image Quality Assessment, Automated Machine Learning.

Abstract: Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a large quantity of data, which may then be enhanced with dedicated image editing software after observation sessions. In this study, we show how Image Quality Assessment can be useful for automatically rating astronomical images, and we also develop a dedicated model by using Automated Machine Learning.

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Paper citation in several formats:

Parisot, O.; Bruneau, P. and Hitzelberger, P. (2023). Astronomical Images Quality Assessment with Automated Machine Learning. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 279-286. DOI: 10.5220/0012073800003541

@conference{data23,
author={Olivier Parisot. and Pierrick Bruneau. and Patrik Hitzelberger.},
title={Astronomical Images Quality Assessment with Automated Machine Learning},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012073800003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - Astronomical Images Quality Assessment with Automated Machine Learning
SN - 978-989-758-664-4
IS - 2184-285X
AU - Parisot, O.
AU - Bruneau, P.
AU - Hitzelberger, P.
PY - 2023
SP - 279
EP - 286
DO - 10.5220/0012073800003541
PB - SciTePress