Learning to denoise astronomical images with U-nets
Open accessAbstract
Astronomical images are essential for exploring and understanding the Universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope (HST), are heavily oversubscribed in the Astronomical Community. Images also often contain additive noise, which makes denoising a mandatory step in post-processing the data before further data analysis. In order to maximize the efficiency and information gain in the post-processing of astronomical imaging, we turn to machine learning. We propose Astro U-net, a convolutional neural network for image denoising and enhancement. For a proof-of-concept, we use HST images from Wide Field Camera 3 instrument UV/visible channel with F555W and F606W filters. Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. From these images, we are able to recover $95.9{{\ \rm per\ cent}}$ of stars with an average flux error of $2.26{{\ \rm per\ cent}}$. Furthermore, the images have, on average, 1.63 times higher signal-to-noise ratio than the input noisy images, equivalent to the stacking of at least three input images, which means a significant reduction in the telescope time needed for future astronomical imaging campaigns.
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Vojtekova et al. (2021) showed that Unets can effectively increase exposure time by a factor of two.
“Vojtekova et al. (2021) showed that Unets can effectively increase the exposure time by a factor of two.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Vojtekova et al. (2021)
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
(2016) · Cited by 11,679 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · see citing works
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643 · see citing works
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 · see citing works
SExtractor: Software for source extraction
Bertin, É., & Arnouts, S. (1996) · Astronomy & Astrophysics Supplement Series · doi:10.1051/aas:1996164 · Cited by 8,937 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(2018) · doi:10.1109/cvpr40276.2018 · Cited by 4,769 · see citing works
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 · see citing works
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866 · cited 1× · see citing works
“…has less layers then ours, and therefore is faster to train and test but still obtaining a good PSNR and outperforming traditional approaches such as the Wiener filter (Starck et al. 2002), Richardson-Lucy algorithm (Richardson 1972; Lucy 1974) and Total Variation regularization (Condat 2014).”
Method(1989) · neural information processing systems · Cited by 3,736 · see citing works
Optimization Methods for Large-Scale Machine Learning
Bottou, L., Curtis, F. E., & Nocedal, J. M. (2018) · SIAM Review · doi:10.1137/16m1080173 · Cited by 3,233 · see citing works
Loss Functions for Image Restoration With Neural Networks
Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2016) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2644865 · Cited by 2,659 · see citing works
Searching for activation functions
Ramachandran, P., Zoph, B., & Le, Q. V. (2017) · arXiv · doi:10.48550/arxiv.1710.05941 · Cited by 2,335 · see citing works
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Navab, N., Joachim Hornegger, A., Wells, W. M., & Frangi, A. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9 · Cited by 2,288 · see citing works
Cosmic‐Ray Rejection by Laplacian Edge Detection
van Dokkum, P. G. (2001) · Publications of the Astronomical Society of the Pacific · doi:10.1086/323894 · Cited by 1,631 · see citing works
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,556 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1994) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1994.323798 · Cited by 1,490 · see citing works
A guide to convolution arithmetic for deep learningPreprint
Dumoulin, V., & Visin, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07285 · Cited by 1,223 · see citing works
A Theoretical Analysis of Feature Pooling in Visual Recognition
Boureau, Y.-L., Ponce, J., & LeCun, Y. (2010) · Cited by 1,196 · see citing works
Multi-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057 · see citing works
1 Cosmic-Ray Rejection by Laplacian Edge DetectionPreprint
van Dokkum, P. (2001) · Cited by 795 · see citing works
Semantic Segmentation with Second-Order Pooling
Carreira, J., Caseiro, R., Batista, J. P., & Sminchisescu, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_32 · Cited by 461 · see citing works
Deconvolution in Astronomy: A Review
Starck, J., Pantin, E., & Murtagh, F. D. (2002) · Publications of the Astronomical Society of the Pacific · doi:10.1086/342606 · Cited by 423 · see citing works
Dogo, E. M., Afolabi, O. J., Nwulu, N. I., Twala, B., & Aigbavboa, C. O. (2018) · International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) · doi:10.1109/ctems.2018.8769211 · Cited by 368 · see citing works
Fast Image Processing with Fully-Convolutional Networks
Chen, Q., Xu, J., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.273 · Cited by 358 · see citing works
Self-citationWelling, M., Sebe, N., Matas, J., & Leibe, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0 · Cited by 278 · see citing works
An Improved Median Filtering Algorithm for Image Noise Reduction
Zhu, Y., & Huang, C. (2012) · Physics Procedia · doi:10.1016/j.phpro.2012.03.133 · Cited by 259 · see citing works
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
(2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1 · Cited by 250 · see citing works
Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K. (2017) · Monthly Notices of the Royal Astronomical Society: Letters · doi:10.1093/mnrasl/slx008 · Cited by 246 · see citing works
Max-Pooling Dropout for Regularization of Convolutional Neural Networks
Wu, H., & Gu, X. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26532-2_6 · Cited by 174 · see citing works
Machine Learning in Astronomy: a practical overviewPreprint
Baron, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.07248 · Cited by 130 · see citing works
Fast cosmic web simulations with generative adversarial networks
Rodríguez, A. C., Kacprzak, T., Lucchi, A., Amara, A., Sgier, R., Fluri, J., Hofmann, T., & Refregier, A. R. (2018) · Computational Astrophysics and Cosmology · doi:10.1186/s40668-018-0026-4 · Cited by 120 · see citing works
Artificial Neural Networks — ICANN 2002
Dorronsoro, J. R. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46084-5 · Cited by 117 · see citing works
A Generic Proximal Algorithm for Convex Optimization—Application to Total Variation Minimization
Condat, L. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2322123 · Cited by 109 · see citing works
Fast cosmic web simulations with generative adversarial networks
Rodríguez, A. C., Kacprzak, T., Lucchi, A., Amara, A., Sgier, R., Fluri, J., Hofmann, T., & Refregier, A. R. (2018) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000318965 · Cited by 91 · see citing works
A survey on evaluation methods for image interpolation
Amanatiadis, A. A., & Andreadis, I. T. (2009) · Measurement Science and Technology · doi:10.1088/0957-0233/20/10/104015 · Cited by 87 · see citing works
ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks
Zingales, T., & Waldmann, I. P. (2018) · The Astronomical Journal · doi:10.3847/1538-3881/aae77c · Cited by 86 · see citing works
Zackay, B., & Ofek, E. O. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/836/2/187 · Cited by 61 · see citing works
Astronomical image reconstruction with convolutional neural networks
Flamary, R. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081654 · Cited by 38 · see citing works
Astronomical image denoising using dictionary learning
Beckouche, S., Starck, J. L., & Fadili, J. M. (2013) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201220752 · Cited by 38 · see citing works
Zackay, B., & Ofek, E. O. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/836/2/188 · Cited by 36 · see citing works
Artificial Neural Networks – ICANN 2010
Diamantaras, K., Iliadis, L. S., & Duch, W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4 · Cited by 32 · see citing works
Astronomical Image Denoising Using Dictionary Learning
Beckouche, S., Starck, J., & Fadili, J. M. (2012) · Cited by 28 · see citing works
Efficient Noise Removing based Optimized Smart Dynamic Gaussian Filter
Seddik, H., & Braiek, E. B. (2012) · International Journal of Computer Applications · doi:10.5120/8035-1334 · Cited by 20 · see citing works
A NOVEL APPROACH FOR NOISE REDUCTION IN THE GABOR TIME-FREQUENCY DOMAIN
Pourebrahimi, B., & van der Lubbe, J. C. A. (2009) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0001768000220027 · Cited by 4 · see citing works
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Sweere, S. F., Valtchanov, I., Lieu, M., Vojtekova, A., Verdugo, E., Santos‐Lleó, M., Pacaud, F., Briassouli, A., & Pérez, D. H. C. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2437 · Cited by 33 · see citing works
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Zhang, J., Wang, F., Zhang, H., & Shi, X. (2023) · Optics & Laser Technology · doi:10.1016/j.optlastec.2023.109334 · Cited by 18 · see citing works
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Aladl, M. M., Gouda, K., Hagag, A., & Ayman, A. (2024) · International Conference on Computer Theory and Applications (ICCTA) · doi:10.1109/iccta64612.2024.10974900
Detection of Southern Hemisphere Constellations Using Convolutional Neural Networks
Riffo, V., Flores, S., Chuy-Kan, E., & Ariza, V. (2025) · IEEE Access · doi:10.1109/access.2025.3575093
Enhancing Tensor Based Imputation with CNNS for Astronomical Imagery Data
Nidhya, R., Pabi, D. J. A., Pavithra, V., Poojitha, V., & Sandeep, S. (2025) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs63635.2025.11031495
Enhancing Image Quality Through Adaptive Denoising Using Modified U-Net Architecture
Khanpara, V., & Chokshi, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5751-3_10
Patil, S. K., Šoltinský, T., Maitra, S., & Kulkarni, G. (2026) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stag236
Hoffmann, L. C., Galli, R., Richter, S., Eyüpoglu, I. Y., & Uckermann, O. (2026) · Interdisciplinary Neurosurgery · doi:10.1016/j.inat.2026.102222
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
Vaheb, O., Fabbro, S., & Draper, S. C. (2026) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ae9b6e
Kim, S., Lee, J. H., & Rey, S. (2026) · The Astronomical Journal · doi:10.3847/1538-3881/ae5a8c
Shiroya, N. J., Dey, M., Rana, S. P., & Fu, C. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-18802-1_19
Mugnai, L. V., Yip, K. H., Bocchieri, A., Παπαγεωργίου, Α., Batista, V., Faucoz, O., Syty, A., Tahseen, T. P. A., Pascale, E., & Waldmann, I. P. (2026) · RAS Techniques and Instruments · doi:10.1093/rasti/rzag036
A Deep Learning Approach For Multi-Wavelength Source Deblending And Classification
Rajesh, N., Sreeram, S., & R., R. V. (2026) · 2026 International Conference on Smart Communication and Sustainable Technologies (ICSCST) · doi:10.1109/icscst70031.2026.11709867
- Authors
- Vojtekova, A., Lieu, M., Valtchanov, I., Altieri, B., Old, L., Chen, Q., & Hroch, F.
- Affiliations
- Masaryk University (CZ)
- University of Nottingham (GB)
- European Space Astronomy Centre (ES)
- European Space Agency (FR)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Vojtekova, Antonia
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-18
- Volume
- 503
- Issue
- 3
- Pages
- 3204-3215
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W3098463180
- Cited by
- 63
- References
- 52
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 16 · 2023: 6 · 2022: 9 · 2021: 4
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- U-Net, convolutional neural networks, Hubble Space Telescope, WFC3, image enhancement, signal-to-noise ratio, star detection
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