Resolution enhancement of SOHO/MDI Magnetograms

Abstract

Abstract Research on the solar magnetic field and its effects on solar power generation and space weather events has benefited from the continual improvement in instrument resolution and measurement frequency. At the same time, expanding and integrating historical observational data along the timeline is also of significant importance for studying the variations in the solar magnetic field. In astronomical data processing, super-resolution reconstruction refers to the process of using a large amount of training data to learn the nonlinear mapping relationship between low-resolution and high-resolution images in order to obtain higher-resolution astronomical images. This paper belongs to the application research of high-dimensional nonlinear regression. We use deep learning models to perform SR modeling on the SOHO/MDI magnetogram and SDO/HMI magnetogram, achieving reliable resolution enhancement of the full-disk MDI magnetogram and improving the resolution of the image to obtain more detailed information. We use a total of 9717 pairs of data that meet the selection criteria from April 2010 to February 2011 as the training set, 1332 pairs of data from March 2011 as the validation set, and 1034 pairs of data from April 2011 as the testing set. After data preprocessing, LR images of size 128*128 are randomly cropped from the MDI full-disk magnetogram, and corresponding HR images of size 512*512 are cropped from the HMI full-disk magnetogram for model training. Through testing, this study achieved reliable 4x super-resolution reconstruction of the full-disk MDI magnetogram. From the correlation coefficient, the MESR model results (0.94) are highly correlated with the HMI target magnetogram. At the same time, this method achieved the best PSNR, SSIM, MAE and RMSE scores, indicating that the MESR model can effectively reconstruct the magnetogram.