Solar Line-of-Sight Magnetograms Super-Resolution Using Deep Neural Networks

Image super-resolution is a branch of image processing that is concerned with enhancing the spatial resolution and quality of images by learning the intrinsic details and relations between the lower resolution input and the higher resolution output images. It is widely accepted as an ill-posed problem, which has seen tremendous advancements with deep learning based models. In this work, we present two magnetogram super resolution models, Sub-Pixel Convolutional Neural Network (CNN) and Enhanced Deep Residual Networks (ResNet), which can be used for improving the spatial resolution of solar magnetograms. While the ill-posed nature of problem is still a challenge, there are several application areas, including space weather prediction, which can greatly benefit from the improved spatial resolution of solar magnetograms. We show that through our experimental evaluation our models perform better than baselines and Sub-Pixel CNN super resolution model provides viable results for magnetogram super resolution.