Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit
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Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon–Nyquist sampling theorem. Here, we train a generative adversarial network (GAN) on a sample of 4550 images of nearby galaxies at 0.01 < z < 0.02 from the Sloan Digital Sky Survey and conduct 10× cross-validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance that far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low signal to noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.
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Schawinski et al. (2017) showed that GANs can recover features even after degradation, but this remains a simple experiment since galaxies were previously degraded.
“They show that GANs can recover features even after degradation. This remains however a simple experiment since galaxies were previously degraded.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Schawinski et al. (2017)
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- Authors
- Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Schawinski, Kevin
- Zhang, Ce
- Venue
- Monthly Notices of the Royal Astronomical Society: Letters
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-01-20
- Volume
- 467
- Issue
- 1
- Pages
- L110-L114
- ISSN
- 1745-3925 (ISSN-L)
- 1745-3933
- OpenAlex
- W2572438701
- Cited by
- 246
- References
- 18
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 19 · 2023: 22 · 2022: 35 · 2021: 36 · 2020: 34 · 2019: 30 · 2018: 25 · 2017: 9 · 2016: 1
- FWCI
- 11.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Funders
- National Science Foundation (PP00P2_138979, PP00P2_166159)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- generative adversarial networks, deconvolution, feature recovery, galaxy morphology, Hubble Space Telescope, James Webb Space Telescope, Sloan Digital Sky Survey
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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