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
APPENDIX
A. IMAGE RESTORATION TEST WITH BLANK NOISE-ONLY IMAGES
One of the key requirements of our deep-learning-based restoration model is that the model should not generate any false object images by overinterpreting the noise when the image contains no real astronomical source. To test this, we created blank noise-only images by varying the random seed and the noise level. We used 10 different random seeds, and for each random seed, we generate 1000 images, where the mean and standard deviation of the noise were set to
mimic those of a randomly selected galaxy image from the JWST train dataset. The RS images created from these blank images were carefully inspected. No image was found to contain pseudo-sources. Figure 14 presents the test results.
This paper is available on arxiv under CC BY 4.0 Deed license.
