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Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network

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Abstract

Image super-resolution is an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network for solar image super-resolution. Our method, named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). The ground-truth labels used for training SolarCNN are the LOS magnetograms collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Solar ARs consist of strong magnetic fields in which magnetic energy can suddenly be released to produce extreme space-weather events, such as solar flares, coronal mass ejections, and solar energetic particles. SOHO/MDI covers Solar Cycle 23, which is stronger with more eruptive events than Cycle 24. Enhanced SOHO/MDI magnetograms allow for better understanding and forecasting of violent events of space weather. Experimental results show that SolarCNN improves the quality of SOHO/MDI magnetograms in terms of the structural similarity index measure, Pearson’s correlation coefficient, and the peak signal-to-noise ratio.

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Acknowledgments

The authors thank the handling editor and anonymous referee for constructive comments and suggestions. We also thank members of the Institute for Space Weather Sciences for fruitful discussions. SOHO is an international cooperation project between ESA and NASA. SDO is a NASA mission. The SolarCNN model is implemented in Python and TensorFlow.

Funding

This work was supported in part by U.S. NSF grants AGS-1927578, AGS-2149748, AGS-2228996, and OAC-2320147.

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Authors and Affiliations

  1. Institute for Space Weather Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102-1982, USA

    Chunhui Xu, Jason T. L. Wang, Haimin Wang, Haodi Jiang, Qin Li, Yasser Abduallah & Yan Xu

  2. Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102-1982, USA

    Chunhui Xu, Jason T. L. Wang & Yasser Abduallah

  3. Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102-1982, USA

    Haimin Wang, Qin Li & Yan Xu

  4. Big Bear Solar Observatory, New Jersey Institute of Technology, 40386 North Shore Lane, Big Bear City, CA, 92314-9672, USA

    Haimin Wang & Yan Xu

  5. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341-2090, USA

    Haodi Jiang

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  1. Chunhui Xu
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  2. Jason T. L. Wang
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Contributions

J.W. and H.W. conceived the study. C.X. implemented the SolarCNN model. H.J. and Q.L. collected and prepared the data used in this study. All the authors reviewed the manuscript.

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Correspondence to Jason T. L. Wang or Haimin Wang.

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Xu, C., Wang, J.T.L., Wang, H. et al. Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network. Sol Phys 299, 36 (2024). https://doi.org/10.1007/s11207-024-02283-1

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