Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model Approach
Journal:
arXiv
Published Date:
Mar 31, 2025
Abstract
The spatial properties of the solar magnetic field are crucial to decoding
the physical processes in the solar interior and their interplanetary effects.
However, observations from older instruments, such as the Michelson Doppler
Imager (MDI), have limited spatial or temporal resolution, which hinders the
ability to study small-scale solar features in detail. Super resolving these
older datasets is essential for uniform analysis across different solar cycles,
enabling better characterization of solar flares, active regions, and magnetic
network dynamics. In this work, we introduce a novel diffusion model approach
for Super-Resolution and we apply it to MDI magnetograms to match the
higher-resolution capabilities of the Helioseismic and Magnetic Imager (HMI).
By training a Latent Diffusion Model (LDM) with residuals on downscaled HMI
data and fine-tuning it with paired MDI/HMI data, we can enhance the resolution
of MDI observations from 2"/pixel to 0.5"/pixel. We evaluate the quality of the
reconstructed images by means of classical metrics (e.g., PSNR, SSIM, FID and
LPIPS) and we check if physical properties, such as the unsigned magnetic flux
or the size of an active region, are preserved. We compare our model with
different variations of LDM and Denoising Diffusion Probabilistic models
(DDPMs), but also with two deterministic architectures already used in the past
for performing the Super-Resolution task. Furthermore, we show with an analysis
in the Fourier domain that the LDM with residuals can resolve features smaller
than 2", and due to the probabilistic nature of the LDM, we can asses their
reliability, in contrast with the deterministic models. Future studies aim to
super-resolve the temporal scale of the solar MDI instrument so that we can
also have a better overview of the dynamics of the old events.
