Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using
  SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Chunhui Xu Jason T. L. Wang Haimin Wang Haodi Jiang Qin Li Yasser Abduallah Yan Xu
Image super-resolution has been an important subject in image processing and
recognition. Here, we present an attention-aided convolutional neural network
(CNN) 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 (HMI) on board the Solar Dynamics Observatory (SDO). 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
(SSIM), Pearson's correlation coefficient (PCC), and the peak signal-to-noise
ratio (PSNR).