Super-Resolution of Solar Active Region Patches Using Generative Adversarial Networks

Rasha Alshehhi

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


Monitoring solar active region patches from Helioseismic and Magnetic Imager (HMI) instruments is essential for space weather forecasting. However, recovering small bipolar details in HMI patches requires additional pre-processing steps to obtain better quality. This work uses a generative adversarial network, with transposed convolution and super-pixel convolution up-sampling layers, to generate the higher quality of HMI patches. It trains and validates the network based on binary cross-entropy, mean absolute error and multi-scale dice-coefficient functions. It illustrates the performance of the generative method in two image types (magnetogram and continuum intensity patches) from two instruments (SDO/HMI and SOT/NET). It also compares its performance with state-of-the-art methods. The results demonstrate that the generative method produces high-quality images by increasing polarity contrast and retrieving smaller structures.

Original languageEnglish (US)
Title of host publicationImage Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings
EditorsStan Sclaroff, Cosimo Distante, Marco Leo, Giovanni M. Farinella, Federico Tombari
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031064265
StatePublished - 2022
Event21st International Conference on Image Analysis and Processing, ICIAP 2022 - Lecce, Italy
Duration: May 23 2022May 27 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13231 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Image Analysis and Processing, ICIAP 2022


  • Generative adversarial network
  • Multi-scale dice-coefficient
  • Solar active region patches
  • Space weather

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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