Improving the spatial resolution of solar images using super-resolution diffusion generative adversarial networks

Abstract

High-spatial-resolution solar images contribute to the study of small-scale structures on the Sun. The Helioseismic and Magnetic Imager (HMI) conducts continuous full-disk observations of the Sun at a fixed cadence, accumulating a wealth of observational data. However, the spatial resolution of HMI images is not sufficient to analyze the small-scale structures of solar activity. We present a new super-resolution (SR) method based on generative adversarial networks (GANs) and denoising diffusion probabilistic models (DDPMs) that can increase the spatial resolution of HMI images by a factor four. We propose a method called super-resolution diffusion GANs (SDGAN), which combines GANs and DDPMs for the SR reconstruction of HMI images. SDGAN progressively maps low-resolution (LR) images to high-resolution (HR) images through a conditional denoising process. It employs conditional GANs to simulate the denoising distribution and optimizes model results using nonsaturating adversarial loss and perceptual loss. This approach enables fast and high-quality reconstruction of solar images. We used high-spatial-resolution images from the Goode Solar Telescope (GST) as HR images and created a data set consisting of paired images from HMI and GST. We then used this data set to train SDGAN for the purpose of reconstructing HMI images with four times the original spatial resolution. The experimental results demonstrate that SDGAN can obtain high-quality HMI reconstructed images with just four denoising steps.