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
4.2. Visual Inspection
Figure 5 shows several image restoration cases in ascending order of rms noise level. We select examples, which have rich substructures, low-surface brightness features, and extended morphologies. The visual inspection indicates remarkable improvements in both resolution and noise level. Detailed substructures such as starforming clumps and spiral arms have been restored with high fidelity. Also, the restored overall morphological features such ellipticity and core size are consistent with those of the GT images. In addition, the low-surface brightness edges are restored remarkably well. Finally, we note that the performance is not very sensitive to the input noise level, which in this example varies by a factor of 3.
This paper is available on arxiv under CC BY 4.0 Deed license.
