Astronomical Images Quality Assessment with Automated Machine Learning

Autor:	Parisot, Olivier, Bruneau, Pierrick, Hitzelberger, Patrik
Rok vydání:	2023
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.5220/0012073800003541
Popis:	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. Comment: 8 pages, accepted at DATA2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.10617 Zobrazit plný text záznamu View this record from Arxiv