Astronomical Images Quality Assessment with Automated Machine Learning
This addresses the need for efficient data management in Electronically Assisted Astronomy, but it is incremental as it applies existing Automated Machine Learning techniques to a new domain-specific dataset.
The study tackled the problem of automatically rating astronomical images by applying Image Quality Assessment and developed a dedicated model using Automated Machine Learning, resulting in a method for automated rating without specifying concrete performance numbers.
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.