edu.ucar.opensky::articles:43330 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T17:51:53.131996 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d71n85ht https://doi.org/10.48550/arXiv.1412.6980 https Related Preprint #1 Adam: A Method for Stochastic Optimization https://doi.org/10.48550/arXiv.1511.06434 https Related Preprint #2 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks https://doi.org/10.48550/arXiv.1607.08022 https Related Preprint #3 Instance Normalization: The Missing Ingredient for Fast Stylization https://doi.org/10.48550/arXiv.1710.10196 https Related Preprint #4 Progressive Growing of GANs for Improved Quality, Stability, and Variation https://doi.org/10.48550/arXiv.1901.09024 https Related Preprint #5 Diversity-Sensitive Conditional Generative Adversarial Networks https://doi.org/10.48550/arXiv.1911.01490 https Related Preprint #6 Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \&amp; Losses https://doi.org/10.5281/zenodo.14405994 https Related Software #1 A deep learning framework for instrument-to-instrument translation of solar observation data A deep learning framework for instrument-to-instrument translation of solar observation data 2025-04-02T00:00:00Z publication https://n2t.net/ark:/85065/d71n85ht https://doi.org/10.1038/s41467-025-58391-4 Jarolim, Robert author Veronig, A. M. author Pötzi, W. author Podladchikova, T. author UCAR/NCAR - Library publisher <p><span style="-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);color:rgb(34, 34, 34);display:inline !important;float:none;font-family:Harding, Palatino, serif;font-size:18px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;">The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not benefit from this trend, and the joint use of data sets from different instruments is typically limited by differences in calibration and quality. We provide a deep learning framework for Instrument-To-Instrument translation of solar observation data, enabling homogenized data series of multi-instrument data sets. This is achieved by unpaired domain translations with Generative Adversarial Networks, which eliminate the need for spatial or temporal overlap to relate instruments. We demonstrate that the available data sets can directly profit from instrumental improvements, by applying our method to four different applications of ground- and space-based solar observations. We obtain a homogenized data series of 24 years of space-based observations of the solar EUV corona and line-of-sight magnetic field, solar full-disk observations with increased spatial resolution, real-time mitigation of atmospheric degradations in ground-based observations, and unsigned magnetic field estimates from the solar far-side based on EUV imagery. The direct comparison to simultaneous high-quality observations shows that our method produces images that are perceptually similar, and enables more homogeneous multi-instrument data sets without the requirement of spatial or temporal alignment.</span></p> UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <span style="font-family:Arial;font-size:10pt;font-style:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span> None eng geoscientificInformation