Journal article 166 views 16 downloads
Removing cloud shadows from ground-based solar imagery
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Adeline Paiement,
Jean Aboudarham
Machine Vision and Applications, Volume: 35, Issue: 6
Swansea University Author:
Jay Paul Morgan
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© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License.
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DOI (Published version): 10.1007/s00138-024-01607-2
Abstract
The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows...
| Published in: | Machine Vision and Applications |
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| ISSN: | 0932-8092 1432-1769 |
| Published: |
Springer Science and Business Media LLC
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67663 |
| Abstract: |
The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds. Quantitative assessments are obtained through image quality indices (RMSE, PSNR, SSIM, and FID). We demonstrate improved results with regards to the traditional cloud removal technique and a sparse coding baseline, on different cloud types and textures. |
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| Keywords: |
Image cleaning; Solar imaging; Deep learning; U-Net; C-GAN |
| College: |
Faculty of Science and Engineering |
| Funders: |
Agence National de la Recherche (ANR grant No ANR-20-CE23-0014-01) |
| Issue: |
6 |