A Multibranch Deep Neural Network for the Superresolution of Solar Magnetograms

The existing superresolution (SR) models for solar magnetograms are mostly borrowed from the SR models for natural images. They are less effective for processing solar magnetograms with a very large dynamic range and very rich image features. In this paper, a multibranch superresolution (MBSR) model is specially designed for solar magnetograms. First, we split a low-resolution magnetogram into a group of overlapping image patches, and classify them into three categories according to magnetic flux intensity, namely simple, medium, and complex. Then, image patches of each category are fed into the corresponding branch of the MBSR network, the lightweight branch for simple image patches and the heavyweight one for complex image patches. The advantage of such a strategy is twofold. On the one hand, active regions are allocated more computational resources to train a heavyweight branch more fully, while quiet regions are allocated fewer computational resources to train a lightweight branch for saving computational resources. On the other hand, a lightweight network with a simple nonlinear function is preferable to simple regions, while a heavyweight one may be underfitting. Additionally, to verify the effectiveness of the proposed model, a magnetic field structure similarity metric is proposed to measure the artifacts of the generated high-resolution (HR) magnetograms. Experimental results show that the proposed MBSR model generates HR magnetograms highly consistent with the HMI ones, and achieves the best performance over five objective metrics, including peak signal-to-noise ratio and structure similarity, etc.