Generation of Solar UV and EUV Images from SDO /HMI Magnetograms by Deep Learning

In this Letter, we apply deep-learning methods to the image-to-image translation from solar magnetograms to solar ultraviolet (UV) and extreme UV (EUV) images. For this, We consider two convolutional neural network models with different loss functions, one (Model A) is with L1 loss (L1), and the other (Model B) is with L1 and cGAN loss (LcGAN). We train the models using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) nine-passband (94, 131, 171, 193, 211, 304, 335, 1600, and 1700 Å) UV/EUV images and their corresponding SDO/Helioseismic and Magnetic Imager (HMI) line-of-sight (LOS) magnetograms from 2011 to 2016. We evaluate the models by comparing pairs of SDO/AIA images and the corresponding ones generated in 2017. Our main results from this study are as follows. First, the models successfully generate SDO/AIA-like solar UV and EUV images from SDO/HMI LOS magnetograms. Second, in view of three metrics (pixel-to-pixel correlation coefficient, relative error, and the percentage of pixels having errors less than 10%), the results from Model A are mostly comparable or slightly better than those from Model B. Third, in view of the rms contrast measure, the generated images by Model A are much more blurred than those by Model B because of LcGAN specialized for generating realistic images.