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High Resolution Solar Image Generation Using Generative Adversarial Networks

Author

Listed:
  • Ankan Dash

    (New Jersey Institute of Technology)

  • Junyi Ye

    (New Jersey Institute of Technology)

  • Guiling Wang

    (New Jersey Institute of Technology)

  • Huiran Jin

    (New Jersey Institute of Technology)

Abstract

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation.

Suggested Citation

  • Ankan Dash & Junyi Ye & Guiling Wang & Huiran Jin, 2024. "High Resolution Solar Image Generation Using Generative Adversarial Networks," Annals of Data Science, Springer, vol. 11(5), pages 1545-1561, October.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:5:d:10.1007_s40745-022-00436-2
    DOI: 10.1007/s40745-022-00436-2
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    References listed on IDEAS

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    1. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    2. Suellen Teixeira Zavadzki de Pauli & Mariana Kleina & Wagner Hugo Bonat, 2020. "Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction," Annals of Data Science, Springer, vol. 7(4), pages 613-628, December.
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    Cited by:

    1. R. Jarolim & A. M. Veronig & W. Pötzi & T. Podladchikova, 2025. "A deep learning framework for instrument-to-instrument translation of solar observation data," Nature Communications, Nature, vol. 16(1), pages 1-19, December.

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