@article { , title = {Deep learning-based super-resolution and de-noising for XMM-newton images}, 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.}, doi = {10.1093/mnras/stac2437}, eissn = {1365-2966}, issn = {0035-8711}, issue = {3}, journal = {Monthly Notices of the Royal Astronomical Society}, pages = {4054-4069}, publicationstatus = {Published}, publisher = {Oxford University Press (OUP)}, url = {https://nottingham-repository.worktribe.com/output/10918660}, volume = {517}, keyword = {Space and Planetary Science, Astronomy and Astrophysics}, year = {2022}, author = {Sweere, Sam F. and Valtchanov, Ivan and Lieu, Maggie and Vojtekova, Antonia and Verdugo, Eva and Santos-Lleo, Maria and Pacaud, Florian and Briassouli, Alexia and Cámpora Pérez, Daniel} }