Deep Learning-Based Super-Resolution and De-Noising for XMM-Newton Images

TL;DR

This paper introduces deep learning models for super-resolution and de-noising of XMM-Newton X-ray images, significantly improving image quality and feature detection, thus enhancing the scientific utility of the telescope's data.

Contribution

First application of machine learning for super-resolution and de-noising of X-ray astronomical images from XMM-Newton, with models trained on realistic simulations.

Findings

XMM-SuperRes doubles the spatial resolution of images.

XMM-DeNoise enhances images to simulate longer exposure times.

Image quality improved by 8.2% in PSNR on real data.

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 2.5x the input exposure time from 20 to 50 ks. When tested on real images, DN improves the image quality by 8.2%, 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.