Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916781045579776 |
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| author | Schirninger, Christoph Jarolim, Robert Veronig, Astrid M. Kuckein, Christoph |
| author_facet | Schirninger, Christoph Jarolim, Robert Veronig, Astrid M. Kuckein, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations Schirninger, Christoph Jarolim, Robert Veronig, Astrid M. Kuckein, Christoph Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition Computational Physics 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. |
| title | Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition Computational Physics |
| url | https://arxiv.org/abs/2506.04781 |