Olivier Parisot

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.

Olivier Parisot, Mahmoud Jaziri

Amateur and professional astronomers can easily capture a large number of deep sky images with recent smart telescopes. However, afterwards verification is still required to check whether the celestial objects targeted are actually visible in the images produced. Depending on the magnitude of the targets, the observation conditions and the time during which the data is captured, it is possible that only stars are present in the images. In this study, we propose an approach based on explainable Artificial Intelligence to automatically detect the presence and position of captured objects. -- -- Grâce à l'apport des télescopes automatisés grand public, les astronomes amateurs et professionnels peuvent capturer facilement une grande quantité d'images du ciel profond (comme par exemple les galaxies, nébuleuses, ou amas globulaires). Néanmoins, une vérification reste nécessaire à postériori pour vérifier si les objets célestes visés sont effectivement visibles dans les images produites: cela dépend notamment de la magnitude des cibles, des conditions d'observation mais aussi de la durée pendant laquelle les données sont capturées. Dans cette étude, nous proposons une approche basée sur l'IA explicable pour détecter automatiquement la présence et la position des objets capturés.

Olivier Parisot, Thomas Tamisier

Reproducible images preprocessing is important in the field of computer vision, for efficient algorithms comparison or for new images corpus preparation. In this paper, we propose a method to obtain an explicit and ordered sequence of transformations that improves a given image: the computation is performed via a nature-inspired optimization algorithm based on quality assessment techniques. Preliminary tests show the impact of the approach on different state-of-the-art data sets. -- L'application de prétraitements explicites et reproductibles est fondamentale dans le domaine de la vision par ordinateur, pour pouvoir comparer efficacement des algorithmes ou pour préparer un nouveau corpus d'images. Dans cet article, nous proposons une méthode pour obtenir une séquence reproductible de transformations qui améliore une image donnée: le calcul est réalisé via un algorithme d'optimisation inspirée par la nature et basé sur des techniques d'évaluation de la qualité. Des tests montrent l'impact de l'approche sur différents ensembles d'images de l'état de l'art.

Olivier Parisot

Recent technological advances in astronomy, particularly the growing popularity of smart telescopes for the general public, make it possible to develop highly effective detection solutions that are accessible to a wide audience, rather than being reserved for major scientific observatories. Published in 2023, DeepSpaceYoloDataset is a collection of annotated images created to train YOLO-based models for detecting Deep Sky Objects, particularly suited for Electronically Assisted Astronomy. In this paper, we present an update to DeepSpaceYoloDataset with the addition of a new split, test2026, designed to evaluate detection models with a greater diversity of images.

Olivier Parisot, Mahmoud Jaziri

The growing negative impact of the visibility of satellites in the night sky is influencing the practice of astronomy and astrophotograph, both at the amateur and professional levels. The presence of these satellites has the effect of introducing streaks into the images captured during astronomical observation, requiring the application of additional post processing to mitigate the undesirable impact, whether for data loss or cosmetic reasons. In this paper, we show how we test and adapt various Deep Learning approaches to detect streaks in raw astronomical data captured between March 2022 and February 2023 with smart telescopes.

Olivier Parisot, Diogo Ramalho Fernandes

Astronomical surveys and the growing involvement of amateur astronomers are producing more sky images than ever before, and this calls for automated processing methods that are accurate and robust. Detecting Deep Sky Objects -- such as galaxies, nebulae, and star clusters -- remains challenging because of their faint signals and complex backgrounds. Advances in Computer Vision and Deep Learning now make it possible to improve and automate this process. In this paper, we present the training and comparison of different detection models (YOLO, RET-DETR) on smart telescope images, using High-Performance Computing (HPC) to parallelise computations, in particular for robustness testing.