Astronomical Images Quality Assessment with Automated Machine Learning

Olivier Parisot, Pierrick Bruneau, Patrik Hitzelberger
11/17/2023
astro-ph.IMcs.CV

No confident code implementation found for this paper. 3 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

An Image Quality Evaluation and Masking Algorithm Based On Pre-trained Deep Neural Networks

86% match2024

Image Restoration with Point Spread Function Regularization and Active Learning

85% match2023

Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning

85% match2023

AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations

82% match2024

Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts

82% match2023

Detecting streaks in smart telescopes images with Deep Learning

82% match2025

Cite this paper

@article{parisot2023astronomical,
  title  = {Astronomical Images Quality Assessment with Automated Machine Learning},
  author = {Olivier Parisot and Pierrick Bruneau and Patrik Hitzelberger},
  year   = {2023},
  eprint = {2311.10617},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.10617v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2311.10617.svg)](https://codeofpaper.com/paper/2311.10617)

Discussion