The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
The Sloan Digital Sky Survey: Technical Summary
York, D., Adelman, J., Anderson, J., Anderson, S., Annis, J., Bahcall, N., Bakken, J., Barkhouser, R., Bastian, S., Berman, E., et al. (2000) · The Astronomical Journal · doi:10.1086/301513 · Cited by 8,368
Communication in the presence of noise
Shannon, C. E. (1949) · Proceedings of the IRE · doi:10.1109/jrproc.1949.232969 · Cited by 6,539
Bayesian-Based Iterative Method of Image Restoration*
Richardson, W. H. (1972) · Journal of the Optical Society of America · doi:10.1364/josa.62.000055 · Cited by 4,160
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866
Certain Topics in Telegraph Transmission Theory
Nyquist, H. (1928) · Transactions of the American Institute of Electrical Engineers · doi:10.1109/t-aiee.1928.5055024 · Cited by 3,134
Generative Adversarial Text to Image Synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494
THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III
Alam, S., Albareti, F. D., Prieto, C. A., Anders, F., Anderson, S. F., Anderton, T., Andrews, B. H., Armengaud, E., Aubourg, É., Bailey, S., et al. (2015) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/219/1/12 · Cited by 2,491
Communications in the presence of noise
Shannon, C. E. (1949) · Medical Entomology and Zoology · Cited by 1,180
LSST Science Book, Version 2.0Preprint
LSST Dark Energy Science Collaboration, Abell, P., Allison, J., Anderson, S. F., Andrew, J., Angel, J. R. P., Armus, L., Arnett, D., Asztalos, S. J., Axelrod, T. S., et al. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0912.0201 · Cited by 905
Research works printed but not linked: 5
- Buta, 1996, Fund. Cosmic Phys., 17, 95
- Courbin, 1999, PhD thesis
- Goodfellow, 2014, Generative Adversarial Nets, NIPS, 2672
- Letawe, 2008, Mem. Soc. Astron. Ital., 79, 1251
- Xu, 2014, Deep Convolutional Neural Network for Image Deconvolution. Advances in Neural Information Processing Systems (NIPS), 1790
Cited by
Sorted by citations
Items 201–242 of 242
Structural Prior-Guided Weighted Low-Rank Denoising for Short-Wave Infrared Star Images
Wu, C., Wang, K., Wang, T., Du, G., Li, X., & Chen, F. (2026) · Sensors · doi:10.3390/s26061980 · Cited by 1
Lanzoni, D., Pierre-Louis, O., Bergamaschini, R., & Montalenti, F. (2026) · Acta Materialia · doi:10.1016/j.actamat.2026.122149 · Cited by 1
Pushing the technical frontier: From overwhelmingly large data sets to machine learning
Acquaviva, V. (2019) · Proceedings of the International Astronomical Union · doi:10.1017/s1743921319003077 · Cited by 1
Metodologia nauk – dzisiaj i jutro
Bremer, J. (2020) · Zagadnienia Naukoznawstwa · doi:10.12775/zn.2019.003 · Cited by 1
Parker, J. G., Servati, M., Diller, E. E., Cao, S., Ho, C. Y., Lober, R. M., & Cohen‐Gadol, A. (2022) · Medical Physics · doi:10.1002/mp.16059 · Cited by 1
Cecotti, H., & Jha, G. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9181-1_52 · Cited by 1
How Good Is Your Scientific Data Generative Model?
Yang, Y., Gremillion, B., Zhang, X., Lin, Y.-Z., Wohlberg, B., & Guan, Q. (2020) · Workshop on Machine Learning in High Performance Computing Environments · doi:10.1109/mlhpcai4s51975.2020.00018 · Cited by 1
Neural Networks and Digital Arts: Some Reflections
Costa, R. A. V., & Schiavoni, F. L. (2022) · Journal of Engineering Research and Sciences · doi:10.55708/js0101002 · Cited by 1
Black Hole Weather Forecasting Using Deep Learning
Pereira, R. D. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.14.2020.tde-23102020-170517 · Cited by 1
Generative Adversarial Networks for Astronomical Images GenerationPreprint
Coccomini, D. A., Messina, N., Gennaro, C., & Falchi, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.11578 · Cited by 1
Generative Adversarial Neural Networks for the generation of High Resolution Galaxy Images.
Cabrera, G. (2023) · doi:10.29393/tmudec-3fa1ga3 · Cited by 1
Multimodal ENSO ForecastPreprint
Naisipour, M., Saeedpanah, I., & Adib, A. (2024) · Research Square · doi:10.21203/rs.3.rs-3474635/v1 · Cited by 1
Generative Adversarial Network and Retinal Image Segmentation
Iqbal, T., & Ali, H. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-40977-7_21
The Lithium Plateau in Super Metal-Rich Stars
Lee-Brown, D. B. (2018) · KU ScholarWorks (The University of Kansas)
Novel Approach for Classification of Fingerprints using Few-shot Classifier
Patil, V., & Ingle, D. R. (2021) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon51891.2021.9645833
Choudhury, N. (2022) · Preprints.org · doi:10.20944/preprints202202.0057.v1
Research on Galaxy Map Reconstruction Method Based on Deep Learning
吴, 康. (2022) · Software Engineering and Applications · doi:10.12677/sea.2022.115118
The performance analysis of generative adversarial networks
Shi, Y., & Zhou, Y. (2023) · International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) · doi:10.1117/12.2656002
Deep Learning for Efficient High-Dimensional Inference in AstrophysicsPreprint
Srivastava, M. (2023) · doi:10.31219/osf.io/7wyjt
Метод генерации крупномасштабных неоднородных текстур
Вяткин, С. И., & Долговесов, Б. С. (2022) · doi:10.18411/sdntp-07-2022-01
Frischia, S. D., & Dolci, M. (2024) · SPIE Software and Cyberinfrastructure for Astronomy · doi:10.1117/12.3018759
Wu, M., & Bu, Y. (2024) · Publications of the Astronomical Society of Japan · doi:10.1093/pasj/psae097
Shan, Q., Liu, C., Qiu, B., & Pan, Z.-R. (2024) · International Conference on New Trends in Computational Intelligence (NTCI) · doi:10.1109/ntci64025.2024.10776520
Astronomical Image Denoising Using AttentionGAN
Naufal, F. Z., Rachmadi, M. F., & Krisnadhi, A. A. (2025) · Syntax literate : jurnal ilmiah Indonesia · doi:10.36418/syntax-literate.v10i1.13154 · Cited by 1
Vasudev, V., Shalu, A., & Rajesh, M. V. (2024) · International Conference on Advancement in Renewable Energy and Intelligent Systems (AREIS) · doi:10.1109/areis62559.2024.10893643
Zhang, Y., Shen, C., Jiang, X., Qiu, B., Luo, A.-L., Ren, F., & Chen, Y.-L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112119
Capturing Star Formation Activity from Compressed Photometric Images of Galaxies
Oh, K., & Turp, M. D. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adf63d
Ismaili, Z., Habibi, O., Chemmakha, M., & Lazaar, M. (2025) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita67914.2025.11273687
Ansah-Narh, T., Tedongmo, J. L., Tandoh, J. B., Imara, N., & Nortey, E. N. N. (2025) · Astronomy and Computing · doi:10.1016/j.ascom.2025.101018
AI and Social IoT for Autonomous Space Missions and Exoplanet Discovery
Shelke, A., Yadav, M., & Saxena, A. C. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-10122-8_14
Cross-domain convergence of generative models: From biomedical to astronomical applications
Ghodhbani, H., Rimer, S., Ouahada, K., & Alimi, A. M. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.100841
A Realistic Synthetic Galaxy Image Superresolution Dataset Based on IllustrisTNG
Miao, J., Tu, L., Liu, H., Liu, H., Jian, Z., Ding, Y., & Xu, Z. (2026) · Research in Astronomy and Astrophysics · doi:10.1088/1674-4527/ae3bdc
Chen, W., He, W., & Wei, X. (2026) · Multimedia Systems · doi:10.1007/s00530-025-02182-w
Elevating Cosmological Image Clarity with GAN-Based Super-Resolution
Sangore, R. B., Nalavade, J. E., Pujeri, R. V., Buchade, A., & Katakdound, V. S. (2025) · IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) · doi:10.1109/icbds67396.2025.11377689
Analysis of the impact of machine learning algorithms on the quality of generated sounds
Pedrycz, K., & Pikula, M. (2026) · Journal of Computer Sciences Institute · doi:10.35784/jcsi.8451
Bayesian statistical inference and deep learning for primordial cosmology and cosmic acceleration
Ramanah, D. K. (2019) · theses.fr (ABES) · doi:10.70675/454bb359z4ed4z4eebzbc32zed2ada8748c5
Learning salient information with neural networks for cosmological analysis
Lin, Q. (2021) · theses.fr (ABES) · doi:10.70675/044e7500zfd93z4c6az8457z4809ad535dae
Kim, S., Lee, J. H., & Rey, S. (2026) · The Astronomical Journal · doi:10.3847/1538-3881/ae5a8c
Vasudev, V., Jayakumar, A. G., & Ismail, S. (2026) · Journal of Astrophysics and Astronomy · doi:10.1007/s12036-026-10173-0
From DESI to Euclid: A Generative Bridge to Improve Measurements of Galaxy Structure
Ye, R., & Shen, S. (2026) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ae9cbf
Metadata
- 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
- 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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
