Deep learning-based super-resolution and de-noising for XMM-newton images
| Autor: | Sam F Sweere, Ivan Valtchanov, Maggie Lieu, Antonia Vojtekova, Eva Verdugo, Maria Santos-Lleo, Florian Pacaud, Alexia Briassouli, Daniel Cámpora Pérez |
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| Přispěvatelé: | RS: FSE DACS, Dept. of Advanced Computing Sciences |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
High Energy Astrophysical Phenomena (astro-ph.HE)
ILLUSTRISTNG SIMULATIONS Astrophysics::High Energy Astrophysical Phenomena FOS: Physical sciences techniques: high angular resolution Astronomy and Astrophysics techniques: image processing X-rays: general DECONVOLUTION CHANDRA Space and Planetary Science Computer Science::Computer Vision and Pattern Recognition SIMILARITY Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - High Energy Astrophysical Phenomena CLUSTERS Instrumentation and Methods for Astrophysics (astro-ph.IM) PHOTON IMAGING CAMERA |
| Zdroj: | Monthly Notices of the Royal Astronomical Society, 517(3), 4054-4069. Oxford University Press |
| ISSN: | 0035-8711 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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