{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enhancing-sdohmi-images-using-deep-learning","title":"Enhancing SDO/HMI images using deep learning","arxiv_id":"1706.02933","date":"2017-06-09","proceeding":null,"authors":["C. J. Diaz Baso","A. Asensio Ramos"],"abstract":"The Helioseismic and Magnetic Imager (HMI) provides continuum images and\nmagnetograms with a cadence better than one per minute. It has been\ncontinuously observing the Sun 24 hours a day for the past 7 years. The obvious\ntrade-off between full disk observations and spatial resolution makes HMI not\nenough to analyze the smallest-scale events in the solar atmosphere. Our aim is\nto develop a new method to enhance HMI data, simultaneously deconvolving and\nsuper-resolving images and magnetograms. The resulting images will mimic\nobservations with a diffraction-limited telescope twice the diameter of HMI.\nOur method, which we call Enhance, is based on two deep fully convolutional\nneural networks that input patches of HMI observations and output deconvolved\nand super-resolved data. The neural networks are trained on synthetic data\nobtained from simulations of the emergence of solar active regions. We have\nobtained deconvolved and supper-resolved HMI images. To solve this ill-defined\nproblem with infinite solutions we have used a neural network approach to add\nprior information from the simulations. We test Enhance against Hinode data\nthat has been degraded to a 28 cm diameter telescope showing very good\nconsistency. The code is open source.","url_abs":"http://arxiv.org/abs/1706.02933v2","url_pdf":"http://arxiv.org/pdf/1706.02933v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"enhancing-sdohmi-images-using-deep-learning","repo_url":"https://github.com/cdiazbas/enhance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}