Neural blind deconvolution to reconstruct high-resolution ground-based solar observations
This paper introduces a novel physics-informed neural network approach for ground-based solar image reconstruction that simultaneously estimates the atmospheric point spread function and the high-resolution intensity distribution, outperforming state-of-the-art methods in revealing small-scale solar features.