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
5.2. Restoration of Multi-epoch HST Images and Comparison with Multi-epoch JWST Images
Figure 8b shows four examples. Despite the difference in filter, the RS images look remarkably similar to the JWST images. In particular, when the noise level is relatively low (i.e., top two panels), the restoration in both resolution and low-surface brightness feature is excellent. When the noise level is relatively high, the RS images still resemble the JWST images more closely than their input HST images. We suspect that the correlated noise in the input multi-epoch HST image might have non-negligible impact on the result in this noise regime.
This paper is available on arxiv under CC BY 4.0 Deed license.
