Skip to main content

Research Repository

Advanced Search

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

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


Sam F. Sweere

Ivan Valtchanov

Antonia Vojtekova

Eva Verdugo

Maria Santos-Lleo

Florian Pacaud

Alexia Briassouli

Daniel Cámpora Pérez


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.


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.

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 (OUP)
Peer Reviewed Peer Reviewed
Volume 517
Issue 3
Pages 4054-4069
Keywords Space and Planetary Science, Astronomy and Astrophysics
Public URL
Publisher URL


You might also like

Downloadable Citations