Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Solar eruptions and the solar wind are sources of space weather disturbances, and extreme-ultraviolet (EUV) observations are widely used to research solar activity and space weather forecasts. Fengyun-3E is equipped with the Solar X-ray and Extreme Ultraviolet Imager, which can observe EUV imaging d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/acb3b9 |
| _version_ | 1827830856871313408 |
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| author | Qinglin Yang Zhou Chen Rongxin Tang Xiaohua Deng Jinsong Wang |
| author_facet | Qinglin Yang Zhou Chen Rongxin Tang Xiaohua Deng Jinsong Wang |
| author_sort | Qinglin Yang |
| collection | DOAJ |
| description | Solar eruptions and the solar wind are sources of space weather disturbances, and extreme-ultraviolet (EUV) observations are widely used to research solar activity and space weather forecasts. Fengyun-3E is equipped with the Solar X-ray and Extreme Ultraviolet Imager, which can observe EUV imaging data. Limited by the lower resolution, however, we research super-resolution techniques to improve the data quality. Traditional image interpolation methods have limited expressive ability, while deep-learning methods can learn to reconstruct high-quality images through training on paired data sets. There is a wide variety of super-resolution models. We try these three representative models: Real-ESRGAN combined with generative adversarial networks, residual channel-attention networks (RCAN) based on channel attention, and SwinIR, based on self-attention. Instruments on different satellites differ in observation time, angle, and resolution, so we selected Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 193 Å images with similar wavelengths as a reference and used a feature-based method for image registration to eliminate slight deformations to build training data sets. Finally, we compare the above methods in their evaluation metrics and visual quality. RCAN has the highest peak signal-to-noise ratio and structural similarity evaluation. Real-ESRGAN model is the best in the Learned Perceptual Image Patch Similarity index, and its results visually show that it has more highly detailed textures. The corrected super-resolution results can complement the SDO/AIA data to provide solar EUV images with a higher temporal resolution for space weather forecasting and solar physics research. |
| first_indexed | 2024-03-12T04:34:36Z |
| format | Article |
| id | doaj.art-2b0a13725262405281f2a5e0fffa8bca |
| institution | Directory Open Access Journal |
| issn | 0067-0049 |
| language | English |
| last_indexed | 2024-03-12T04:34:36Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj.art-2b0a13725262405281f2a5e0fffa8bca2023-09-03T09:56:24ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492023-01-0126523610.3847/1538-4365/acb3b9Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar ImagesQinglin Yang0Zhou Chen1https://orcid.org/0000-0003-3271-2515Rongxin Tang2https://orcid.org/0000-0003-0154-3456Xiaohua Deng3Jinsong Wang4School of Mathematics and Computer Sciences, Nanchang University , Nanchang, People's Republic of China; Institute of Space Science and Technology, Nanchang University , Nanchang, People's Republic of China ; chenzhou760@foxmail.comInstitute of Space Science and Technology, Nanchang University , Nanchang, People's Republic of China ; chenzhou760@foxmail.com; Information Engineering School, Nanchang University , Nanchang, People's Republic of China; Key Laboratory of Space Weather, National Center for Space Weather , Meteorological Administration, Beijing, People's Republic of ChinaInstitute of Space Science and Technology, Nanchang University , Nanchang, People's Republic of China ; chenzhou760@foxmail.com; Information Engineering School, Nanchang University , Nanchang, People's Republic of ChinaInstitute of Space Science and Technology, Nanchang University , Nanchang, People's Republic of China ; chenzhou760@foxmail.com; Information Engineering School, Nanchang University , Nanchang, People's Republic of ChinaKey Laboratory of Space Weather, National Center for Space Weather , Meteorological Administration, Beijing, People's Republic of ChinaSolar eruptions and the solar wind are sources of space weather disturbances, and extreme-ultraviolet (EUV) observations are widely used to research solar activity and space weather forecasts. Fengyun-3E is equipped with the Solar X-ray and Extreme Ultraviolet Imager, which can observe EUV imaging data. Limited by the lower resolution, however, we research super-resolution techniques to improve the data quality. Traditional image interpolation methods have limited expressive ability, while deep-learning methods can learn to reconstruct high-quality images through training on paired data sets. There is a wide variety of super-resolution models. We try these three representative models: Real-ESRGAN combined with generative adversarial networks, residual channel-attention networks (RCAN) based on channel attention, and SwinIR, based on self-attention. Instruments on different satellites differ in observation time, angle, and resolution, so we selected Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 193 Å images with similar wavelengths as a reference and used a feature-based method for image registration to eliminate slight deformations to build training data sets. Finally, we compare the above methods in their evaluation metrics and visual quality. RCAN has the highest peak signal-to-noise ratio and structural similarity evaluation. Real-ESRGAN model is the best in the Learned Perceptual Image Patch Similarity index, and its results visually show that it has more highly detailed textures. The corrected super-resolution results can complement the SDO/AIA data to provide solar EUV images with a higher temporal resolution for space weather forecasting and solar physics research.https://doi.org/10.3847/1538-4365/acb3b9Solar ultraviolet emission |
| spellingShingle | Qinglin Yang Zhou Chen Rongxin Tang Xiaohua Deng Jinsong Wang Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images The Astrophysical Journal Supplement Series Solar ultraviolet emission |
| title | Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images |
| title_full | Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images |
| title_fullStr | Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images |
| title_full_unstemmed | Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images |
| title_short | Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images |
| title_sort | image super resolution methods for fy 3e x euvi 195 a solar images |
| topic | Solar ultraviolet emission |
| url | https://doi.org/10.3847/1538-4365/acb3b9 |
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