FCD is designed to work with STIX data; it takes 48 Fourier components (24 real, 24 imaginary) and reconstructs the image corresponding to them.
Below is the comparison of the FCD with other popular STIX reconstruction alorithms.
pip install -r requirements.txt to install all the necessary packages.
- nn includes training and testing of the FCD.
- metrics, data, and utils include relevant utility functions of the project.
Pretrained model of the FCD and a demo of the model are available on HuggingFace.
- Refer to STIX data center to use observational STIX data.
- Refer to STIX data generator to generate your own simulated data.
If this repository proves useful for your research, please cite our work as follows.
@article{SelcukSimsek2025,
author = {Selcuk-Simsek, Merve and Massa, Paolo and Xiao, Hualin and Krucker, S{\"a}m and Csillaghy, Andr{\'e}},
title = {Fourier convolutional decoder: reconstructing solar flare images via deep learning},
journal = {Neural Computing and Applications},
pages = {1--32},
year = {2025},
publisher = {Springer}
doi = {10.1007/s00521-025-11283-6},
url = {https://doi.org/10.1007/s00521-025-11283-6}
}