Deep regression for imaging solar magnetograms using pyramid generative adversarial networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Monitoring a large active region in the farside of the Sun is important for space weather forecasting. However, direct imaging of the farside is currently not available and usually physicists rely on seismic holography to infer farside magnetograms. On other hand, mapping between holography and magnetic images is non-trivial. In this work, Generative Adversarial Network (GAN) is used; which consists of a pyramid of modified pixel2pixel architectures to capture internal distributions at different scales with higher quality. Generative model is trained and evaluated using frontside of Solar Dynamic Observatory (SDO): Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) magnetograms. Farside solar magnetograms from Extreme UltraViolet Imager (EUVI) farside data is also generated. The generative model successfully generates frontside solar magnetograms and outperforms state-of-The art method. It also help to monitor the magnetic changes from farside to frontside using generated solar magnetograms.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages807-815
Number of pages9
ISBN (Electronic)9781728193601
DOIs
StatePublished - Jun 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2020-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
CountryUnited States
CityVirtual, Online
Period6/14/206/19/20

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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