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
6.2. Point Source Recovery Test
In principle, the perfect deconvolution algorithm should restore a point source to a delta function, which is infinitely smaller than a pixel. In traditional deconvolution in the Fourier domain, it is a challenging task because the operation is numerically unstable. The resulting images often exhibit many ringing effects around a bright central peak.
Since we excluded stars from the training dataset, our deep learning model did not explicitly learn to deconvolve point source images. Thus, it is interesting to examine how well our deep learning model, trained with only galaxy images, restore point sources. We perform a point source recovery test as follows. First, we created 1,000 JWST-quality star GT images. Because we did not remove the JWST PSF from the training dataset, the star GT images should not resemble a delta functions but the JWST PSF. We implemented this by convolving a single pixel with a Gaussian whose kernel size matches the JWST PSF. Note that we randomize the positions of the stars within the central 24×24 pixels of the 64 × 64 postage-stamp images. Then, we created their LQ versions by further convolving the GT images with the HST PSF and adding noise. Finally, these LQ images are restored by our deep learning model. To investigate the systematic effect, we stack the 1,000 GT and RS images separately after aligning their centers.
Figure 12 shows that the difference is subtle when we compare the GT (left) and RS (middle) stacks visually. However, the residual image (right) illustrates that the PSF of the RS stack is systematically larger. Thus, we conclude that our deep learning model, trained solely with galaxy images, performs less than ideally for point sources.
This paper is available on arxiv under CC BY 4.0 Deed license.
