Improving the Spatial Resolution of GONG Solar Images to GST Quality Using Deep Learning
This addresses the need for high-resolution solar imaging to capture fine-scale dynamic features like filaments and fibrils, but it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the problem of low spatial resolution in GONG solar images by proposing a GAN-based superresolution approach to enhance them to GST quality, achieving an average MSE of 467.15, RMSE of 21.59, and CC of 0.7794.
High-resolution (HR) solar imaging is crucial for capturing fine-scale dynamic features such as filaments and fibrils. However, the spatial resolution of the full-disk H$α$ images is limited and insufficient to resolve these small-scale structures. To address this, we propose a GAN-based superresolution approach to enhance low-resolution (LR) full-disk H$α$ images from the Global Oscillation Network Group (GONG) to a quality comparable with HR observations from the Big Bear Solar Observatory/Goode Solar Telescope (BBSO/GST). We employ Real-ESRGAN with Residual-in-Residual Dense Blocks and a relativistic discriminator. We carefully aligned GONG-GST pairs. The model effectively recovers fine details within sunspot penumbrae and resolves fine details in filaments and fibrils, achieving an average mean squared error (MSE) of 467.15, root mean squared error (RMSE) of 21.59, and cross-correlation (CC) of 0.7794. Slight misalignments between image pairs limit quantitative performance, which we plan to address in future work alongside dataset expansion to further improve reconstruction quality.