Deep learning-based super-resolution and de-noising for XMM-newton images

Sweere, Sam F.; Valtchanov, Ivan; Lieu, Maggie; Vojtekova, Antonia; Verdugo, Eva; Santos-Lleo, Maria; Pacaud, Florian; Briassouli, Alexia; Cámpora Pérez, Daniel

Abstract

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work, we present the first application of Machine Learning based super-resolution (SR) and de-noising (DN) to enhance X-ray images from the European Space Agency's XMM-Newton telescope. Using XMM-Newton images in band [0.5, 2] keV from the European Photon Imaging Camera pn detector (EPIC-pn), we develop XMM-SuperRes and XMM-DeNoise - deep learning-based models that can generate enhanced SR and DN images from real observations. The models are trained on realistic XMM-Newton simulations such that XMM-SuperRes will output images with two times smaller point-spread function and with improved noise characteristics. The XMM-DeNoise model is trained to produce images with 2.5× the input exposure time from 20 to 50 ks. When tested on real images, DN improves the image quality by 8.2 per cent, as quantified by the global peak-signal-to-noise ratio. These enhanced images allow identification of features that are otherwise hard or impossible to perceive in the original or in filtered/smoothed images with traditional methods. We demonstrate the feasibility of using our deep learning models to enhance XMM-Newton X-ray images to increase their scientific value in a way that could benefit the legacy of the XMM-Newton archive.

Citation

Sweere, S. F., Valtchanov, I., Lieu, M., Vojtekova, A., Verdugo, E., Santos-Lleo, M., …Cámpora Pérez, D. (2022). Deep learning-based super-resolution and de-noising for XMM-newton images. Monthly Notices of the Royal Astronomical Society, 517(3), 4054-4069. https://doi.org/10.1093/mnras/stac2437

• Journal Article Type: Article
• Acceptance Date: Aug 24, 2022
• Online Publication Date: Sep 8, 2022
• Publication Date: Nov 1, 2022
• Deposit Date: Oct 26, 2022
• Publicly Available Date: Oct 27, 2022
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 517
• Issue: 3
• Pages: 4054-4069
• DOI: https://doi.org/10.1093/mnras/stac2437
• Keywords: Space and Planetary Science, Astronomy and Astrophysics
• Public URL: https://nottingham-repository.worktribe.com/output/10918660
• Publisher URL: https://academic.oup.com/mnras/advance-article-abstract/doi/10.1093/mnras/stac2437/6694110?redirectedFrom=fulltext&login=false