Improving the Spatial Resolution of Solar Images Based on an Improved Conditional Denoising Diffusion Probability Model

The quality of solar images plays an important role in the analysis of small events in solar physics. Therefore, the improvement of image resolution based on super-resolution (SR) reconstruction technology has aroused the interest of many researchers. In this paper, an improved conditional denoising diffusion probability model (ICDDPM) based on the Markov chain is proposed for the SR reconstruction of solar images. This method reconstructs high-resolution (HR) images from low-resolution images by learning a reverse process that adds noise to HR images. To verify the effectiveness of the method, images from the Goode Solar Telescope at the Big Bear Solar Observatory and the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory are used to train a network, and the spatial resolution of reconstructed images is 4 times that of the original HMI images. The experimental results show that the performance based on ICDDPM is better than the previous work in subject judgment and object evaluation indexes. The reconstructed images of this method have higher subjective vision quality and better consistency with the HMI images. And the structural similarity and rms index results are also higher than the compared method, demonstrating the success of the resolution improvement using ICDDPM.

Automated summary

This paper proposes an improved conditional denoising diffusion probability model (ICDDPM) based on the Markov chain for the super-resolution reconstruction of solar images. Experimental results show that the method outperforms previous methods in terms of image quality and evaluation metrics.