Super-resolution of SDO/HMI Magnetograms Using Novel Deep Learning Methods

Abstract

Image super-resolution is a technique of enhancing the resolution of an image where a high-resolution (HR) image is reconstructed from a low-resolution (LR) image. In this Letter, we apply two novel deep learning models (residual attention model and progressive GAN model) for enhancing Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) magnetograms. For this, we consider line-of-sight (LOS) magnetograms taken by SDO/HMI as output and their degraded ones with 4 × 4 binning as input. Deep learning networks try to find internal relationships between LR and HR images from the given input and the corresponding output image. We consider SDO/HMI magnetograms from 2014 May to August for training, from 2014 October to December for validation, and 2015 January to March for test. We find that the deep learning models generate higher-quality results than the bicubic interpolation in terms of visual aspects and metrics. We apply this model to a full-resolution SDO/HMI magnetogram and then compare the generated magnetogram with the corresponding Hinode/The Solar Optical Telescope Narrowband Filtergrams (NFI) magnetogram. This comparison shows that the generated magnetogram is consistent with the Hinode one with a high correlation (CC: 0.94) and a high similarity (SSIM: 0.93), which are better than the bicubic method.