Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning
R Jarolim, AM Veronig, W Pötzi… - arXiv preprint arXiv …, 2024 - arxiv.org
The constant improvement of astronomical instrumentation provides the foundation for
scientific discoveries. In general, these improvements have only implications forward in time …
scientific discoveries. In general, these improvements have only implications forward in time …
Single-frame super-resolution of solar magnetograms: Investigating physics-based metrics\& losses
Breakthroughs in our understanding of physical phenomena have traditionally followed
improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence …
improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence …
Using U-Nets to create high-fidelity virtual observations of the solar corona
V Salvatelli, S Bose, B Neuberg… - arXiv preprint arXiv …, 2019 - arxiv.org
Understanding and monitoring the complex and dynamic processes of the Sun is important
for a number of human activities on Earth and in space. For this reason, NASA's Solar …
for a number of human activities on Earth and in space. For this reason, NASA's Solar …
A machine-learning-ready dataset prepared from the solar and heliospheric observatory mission
We present a Python tool to generate a standard dataset from solar images that allows for
user-defined selection criteria and a range of pre-processing steps. Our Python tool works …
user-defined selection criteria and a range of pre-processing steps. Our Python tool works …
ITI for the Sun: Improved intercalibration of multi-instrument heliophysics data series with Instrument-To-Instrument translation
R Jarolim - Proceedings of the 2nd Machine Learning in …, 2022 - ui.adsabs.harvard.edu
In solar physics, the study of long-term evolution typically exceeds the lifetime of single
instruments. Data-driven approaches are limited in terms of homogeneous historical data …
instruments. Data-driven approaches are limited in terms of homogeneous historical data …
Image-quality assessment for full-disk solar observations with generative adversarial networks
R Jarolim, AM Veronig, W Pötzi… - Astronomy & …, 2020 - aanda.org
Context. In recent decades, solar physics has entered the era of big data and the amount of
data being constantly produced from ground-and space-based observatories can no longer …
data being constantly produced from ground-and space-based observatories can no longer …
Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms
Superresolution (SR) aims to increase the resolution of images by recovering detail.
Compared to standard interpolation, deep learning-based approaches learn features and …
Compared to standard interpolation, deep learning-based approaches learn features and …
De-noising SDO/HMI solar magnetograms by image translation method based on deep learning
In astronomy, long-exposure observations are one of the important ways to improve signal-
to-noise ratios (S/Ns). In this Letter, we apply a deep-learning model to de-noise solar …
to-noise ratios (S/Ns). In this Letter, we apply a deep-learning model to de-noise solar …
Improving the spatial resolution of solar images using generative adversarial network and self-attention mechanism
J Deng, W Song, D Liu, Q Li, G Lin… - The Astrophysical …, 2021 - iopscience.iop.org
In recent years, the new physics of the Sun has been revealed using advanced data with
high spatial and temporal resolutions. The Helioseismic and Magnetic Imager (HMI) on …
high spatial and temporal resolutions. The Helioseismic and Magnetic Imager (HMI) on …
Enhancing SDO/HMI images using deep learning
CJD Baso, AA Ramos - Astronomy & Astrophysics, 2018 - aanda.org
Context. The Helioseismic and Magnetic Imager (HMI) provides continuum images and
magnetograms with a cadence better than one per minute. It has been continuously …
magnetograms with a cadence better than one per minute. It has been continuously …