Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Journal article

Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel−1; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel−1; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel−1; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications

Authors: Muñoz-Jaramillo, A; Jungbluth, A; Gitiaux, X; Wright, PJ; Shneider, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Astronomical Society
• Journal: The Astrophysical Journal: Supplement Series
• Volume: 271
• Issue: 2
• Pages: 46-46
• Publication date: 2024-03-26
• DOI: 10.3847/1538-4365/ad12c2
• EISSN: 1538-4365
• ISSN: 0067-0049
• Language: English
• Keywords: Computer science; Cartography; Artificial neural network; Calibration; Leverage (statistics); Interpolation (computer graphics); Deep learning; Artificial intelligence; Physics; Pixel; Image resolution; Image (mathematics); Computer vision; Geography; Benchmark (surveying); Remote sensing