Astronomical image denoising by self-supervised deep learning and restoration processes
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916627277152256 |
|---|---|
| author | Liu, Tie Quan, Yuhui Su, Yingna Guo, Yang Liu, Shu Ji, Haisheng Hao, Qi Gao, Yulong Liu, Yuxia Wang, Yikang Sun, Wenqing Ding, Mingde |
| author_facet | Liu, Tie Quan, Yuhui Su, Yingna Guo, Yang Liu, Shu Ji, Haisheng Hao, Qi Gao, Yulong Liu, Yuxia Wang, Yikang Sun, Wenqing Ding, Mingde |
| contents | Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is designed for denoising single-image, training on it and denoising itself, during which training is costly. In this work we explore training Self2Self on an astronomical image and denoising other images of the same kind, which is suitable for quickly denoising massive images in astronomy. To address the deviation issue, the abnormal pixels whose deviation exceeds a predefined threshold are restored to their initial values. The noise reduction includes training, denoising, restoring and named TDR-method, by which the noise level of the solar magnetograms is improved from about 8 G to 2 G. Furthermore, the TDR-method is applied to galaxy images from the Hubble Space Telescope and makes weak galaxy structures become much clearer. This capability of enhancing weak signals makes the TDR-method applicable in various disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Astronomical image denoising by self-supervised deep learning and restoration processes Liu, Tie Quan, Yuhui Su, Yingna Guo, Yang Liu, Shu Ji, Haisheng Hao, Qi Gao, Yulong Liu, Yuxia Wang, Yikang Sun, Wenqing Ding, Mingde Instrumentation and Methods for Astrophysics Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is designed for denoising single-image, training on it and denoising itself, during which training is costly. In this work we explore training Self2Self on an astronomical image and denoising other images of the same kind, which is suitable for quickly denoising massive images in astronomy. To address the deviation issue, the abnormal pixels whose deviation exceeds a predefined threshold are restored to their initial values. The noise reduction includes training, denoising, restoring and named TDR-method, by which the noise level of the solar magnetograms is improved from about 8 G to 2 G. Furthermore, the TDR-method is applied to galaxy images from the Hubble Space Telescope and makes weak galaxy structures become much clearer. This capability of enhancing weak signals makes the TDR-method applicable in various disciplines. |
| title | Astronomical image denoising by self-supervised deep learning and restoration processes |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2502.16807 |