Authors:
(1) Hyosun park, Department of Astronomy, Yonsei University, Seoul, Republic of Korea;
(2) Yongsik Jo, Artificial Intelligence Graduate School, UNIST, Ulsan, Republic of Korea;
(3) Seokun Kang, Artificial Intelligence Graduate School, UNIST, Ulsan, Republic of Korea;
(4) Taehwan Kim, Artificial Intelligence Graduate School, UNIST, Ulsan, Republic of Korea;
(5) M. James Jee, Department of Astronomy, Yonsei University, Seoul, Republic of Korea and Department of Physics and Astronomy, University of California, Davis, CA, USA.
Table of Links
2 Method
2.1. Overview and 2.2. Encoder-Decoder Architecture
2.3. Transformers for Image Restoration
4 JWST Test Dataset Results and 4.1. PSNR and SSIM
4.3. Restoration of Morphological Parameters
4.4. Restoration of Photometric Parameters
5.2. Restoration of Multi-epoch HST Images and Comparison with Multi-epoch JWST Images
6 Limitations
6.1. Degradation in Restoration Quality Due to High Noise Level
6.2. Point Source Recovery Test
6.3. Artifacts Due to Pixel Correlation
7 Conclusions and Acknowledgements
Appendix: A. Image restoration test with Blank Noise-Only Images
2.4. Implementation Details
We use a transfer learning approach, where a model trained on one dataset is reused for another related dataset. First, we train on the pre-training dataset (simplified galaxy images) for 150,000 iterations, followed by an additional 150,000 iterations on the finetuning dataset (realistic galaxy images). The batch size remains fixed at 64. Additionally, to compare and analyze the performance of training on individual datasets, we also conduct training separately solely based on either the pre-training or finetuning datasets. Our inference model is publically available [1].
This paper is available on arxiv under CC BY 4.0 Deed license.
[1] https://github.com/JOYONGSIK/GalaxyRestoration
