Astronomical Images Quality Assessment with Automated Machine Learning [* Cross-Reference *]

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Olivier Parisot; Pierrick Bruneau; Patrik Hitzelberger, 2026, "Astronomical Images Quality Assessment with Automated Machine Learning [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.KIBD2X, LIST Open Repository, V1
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Description
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. (2023-01-01)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5220/0012073800003541 for the original and latest version of the publication*** (2026-07-01)
Subject Computer and Information Science; Engineering; Physics
Keyword Computer science, Quality (philosophy), Artificial intelligence, Quality assessment, Computer vision, Machine learning, Reliability engineering, Engineering, Evaluation methods
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