Astronomical Images Quality Assessment with Automated Machine Learning
Olivier Parisot Pierrick Bruneau Patrik Hitzelberger
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.