TY - GEN
T1 - Super-Resolution of Solar Active Region Patches Using Generative Adversarial Networks
AU - Alshehhi, Rasha
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Generative adversarial network
KW - Multi-scale dice-coefficient
KW - Solar active region patches
KW - Space weather
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U2 - 10.1007/978-3-031-06427-2_38
DO - 10.1007/978-3-031-06427-2_38
M3 - Conference contribution
AN - SCOPUS:85130978737
SN - 9783031064265
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 451
EP - 462
BT - Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings
A2 - Sclaroff, Stan
A2 - Distante, Cosimo
A2 - Leo, Marco
A2 - Farinella, Giovanni M.
A2 - Tombari, Federico
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Image Analysis and Processing, ICIAP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -