Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations
This work addresses the challenge of real-time, high-quality image reconstruction for ground-based solar astronomy, offering an incremental improvement over existing methods by enhancing robustness in turbulent conditions.
The paper tackles the problem of reconstructing high-resolution solar images from short-exposure bursts degraded by atmospheric turbulence, achieving real-time processing of 100 images into a single high-quality output with improved perceptual robustness.
Large aperture ground based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, observations are limited by Earths turbulent atmosphere, requiring post image corrections. Current reconstruction methods using short exposure bursts face challenges with strong turbulence and high computational costs. We introduce a deep learning approach that reconstructs 100 short exposure images into one high quality image in real time. Using unpaired image to image translation, our model is trained on degraded bursts with speckle reconstructions as references, improving robustness and generalization. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstructions show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image information and achieves the best reconstructions when provided with the full image burst.