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References

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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

Abstract and 1 Introduction

2 Method

2.1. Overview and 2.2. Encoder-Decoder Architecture

2.3. Transformers for Image Restoration

2.4. Implementation Details

3 Data and 3.1. HST Dataset

3.2. GalSim Dataset

3.3. JWST Dataset

4 JWST Test Dataset Results and 4.1. PSNR and SSIM

4.2. Visual Inspection

4.3. Restoration of Morphological Parameters

4.4. Restoration of Photometric Parameters

5 Application to real HST Images and 5.1. Restoration of Single-epoch Images and Comparison with Multi-epoch Images

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

References

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