
@article{Ingaramo2014,
	title        = {Richardson-Lucy Deconvolution as a General Tool for Combining Images with Complementary Strengths},
	author       = {Ingaramo, Maria and York, Andrew and Hoogendoorn, Eelco and Postma, Marten and Shroff, Hari and Patterson, George},
	year         = 2014,
	month        = {03},
	journal      = {Chemphyschem : a European journal of chemical physics and physical chemistry},
	volume       = 15,
	pages        = {},
	doi          = {10.1002/cphc.201300831}
}
@inproceedings{Bouman2016,
	title        = {Computational Imaging for VLBI Image Reconstruction},
	author       = {Bouman, Katherine L. and Johnson, Michael D. and Zoran, Daniel and Fish, Vincent L. and Doeleman, Sheperd S. and Freeman, William T.},
	year         = 2016,
	month        = {June},
	booktitle    = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
}
@inproceedings{Zoran2011,
	title        = {From learning models of natural image patches to whole image restoration},
	author       = {Zoran, Daniel and Weiss, Yair},
	year         = 2011,
	booktitle    = {2011 International Conference on Computer Vision},
	volume       = {},
	number       = {},
	pages        = {479--486},
	doi          = {10.1109/ICCV.2011.6126278}
}
@article{Esch2004,
	title        = {An Image Restoration Technique with Error Estimates},
	author       = {David N. Esch and Alanna Connors and Margarita Karovska and David A. van Dyk},
	year         = 2004,
	month        = {aug},
	journal      = {The Astrophysical Journal},
	publisher    = {American Astronomical Society},
	volume       = 610,
	number       = 2,
	pages        = {1213--1227},
	doi          = {10.1086/421761},
	url          = {https://doi.org/10.1086/421761},
	abstract     = {Image restoration including deconvolution techniques offers a powerful tool to improve resolution in images and to extract information on the multiscale structure stored in astronomical observations. We present a new method for statistical deconvolution, which we call expectation through Markov Chain Monte Carlo (EMC2). This method is designed to remedy several shortfalls of currently used deconvolution and restoration techniques for Poisson data. We use a wavelet-like multiscale representation of the true image to achieve smoothing at all scales of resolution simultaneously, thus capturing detailed features in the image at the same time as larger scale extended features. Thus, this method smooths the image, while maintaining the ability to effectively reconstruct point sources and sharp features in the image. We use a principled, fully Bayesian model-based analysis, which produces extensive information about the uncertainty in the fitted smooth image, allowing assessment of the errors in the resulting reconstruction. Our method also includes automatic fitting of the multiscale smoothing parameters. We show several examples of application of EMC2 to both simulated data and a real astronomical X-ray source.}
}
@inproceedings{Connors2011,
	title        = {{LIRA {\textemdash} The Low-Counts Image Restoration and Analysis Package: A Teaching Version via R}},
	author       = {{Connors}, A. and {Stein}, N.~M. and {van Dyk}, D. and {Kashyap}, V. and {Siemiginowska}, A.},
	year         = 2011,
	month        = jul,
	booktitle    = {Astronomical Data Analysis Software and Systems XX},
	series       = {Astronomical Society of the Pacific Conference Series},
	volume       = 442,
	pages        = 463,
	editor       = {{Evans}, I.~N. and {Accomazzi}, A. and {Mink}, D.~J. and {Rots}, A.~H.},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2011ASPC..442..463C},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Hunter2007,
	title        = {Matplotlib: A 2D graphics environment},
	author       = {Hunter, J. D.},
	year         = 2007,
	journal      = {Computing in Science \& Engineering},
	publisher    = {IEEE COMPUTER SOC},
	volume       = 9,
	number       = 3,
	pages        = {90--95},
	doi          = {10.1109/MCSE.2007.55},
	abstract     = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.}
}
@article{Numpy2020,
	title        = {Array programming with {NumPy}},
	author       = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian Berg and Nathaniel J. Smith and Robert Kern and Matti Picus and Stephan Hoyer and Marten H. van Kerkwijk and Matthew Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and Warren Weckesser and Hameer Abbasi and Christoph Gohlke and Travis E. Oliphant},
	year         = 2020,
	month        = sep,
	journal      = {Nature},
	publisher    = {Springer Science and Business Media {LLC}},
	volume       = 585,
	number       = 7825,
	pages        = {357--362},
	doi          = {10.1038/s41586-020-2649-2},
	url          = {https://doi.org/10.1038/s41586-020-2649-2}
}
@article{Astropy2018,
	title        = {{The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package}},
	author       = {{Astropy Collaboration}},
	year         = 2018,
	month        = sep,
	journal      = {The Astrophysical Journal},
	volume       = 156,
	number       = 3,
	pages        = 123,
	doi          = {10.3847/1538-3881/aabc4f},
	keywords     = {methods: data analysis, methods: miscellaneous, methods: statistical, reference systems, Astrophysics - Instrumentation and Methods for Astrophysics},
	eid          = 123,
	archiveprefix = {arXiv},
	eprint       = {1801.02634},
	primaryclass = {astro-ph.IM},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2018AJ....156..123A},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{scikit-image,
	title        = {scikit-image: image processing in Python},
	author       = {van der Walt, Stéfan and Schönberger, Johannes L. and Nunez-Iglesias, Juan and Boulogne, François and Warner, Joshua D. and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony and the scikit-image contributors},
	year         = 2014,
	month        = {jun},
	journal      = {PeerJ},
	volume       = 2,
	pages        = {e453},
	doi          = {10.7717/peerj.453},
	issn         = {2167-8359},
	url          = {https://doi.org/10.7717/peerj.453},
	keywords     = {Image processing, Reproducible research, Education, Visualization, Open source, Python, Scientific programming},
	abstract     = {scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.}
}
@article{scikit-learn,
	title        = {Scikit-learn: Machine Learning in {P}ython},
	author       = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
	year         = 2011,
	journal      = {Journal of Machine Learning Research},
	volume       = 12,
	pages        = {2825--2830}
}
@article{Richardson1972,
	title        = {{Bayesian-Based Iterative Method of Image Restoration}},
	author       = {{Richardson}, William Hadley},
	year         = 1972,
	month        = jan,
	journal      = {Journal of the Optical Society of America (1917-1983)},
	volume       = 62,
	number       = 1,
	pages        = 55,
	adsurl       = {https://ui.adsabs.harvard.edu/abs/1972JOSA...62...55R},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Lucy1974,
	title        = {{An iterative technique for the rectification of observed distributions}},
	author       = {{Lucy}, L.~B.},
	year         = 1974,
	month        = jun,
	journal      = {Astronomical Journal},
	volume       = 79,
	pages        = 745,
	doi          = {10.1086/111605},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/1974AJ.....79..745L},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Cash1979,
	title        = {{Parameter estimation in astronomy through application of the likelihood ratio.}},
	author       = {{Cash}, W.},
	year         = 1979,
	month        = mar,
	journal      = {The Astrophysical Journal},
	volume       = 228,
	pages        = {939--947},
	doi          = {10.1086/156922},
	keywords     = {Astronomical Models, Data Processing, Parameterization, Statistical Analysis, Confidence Limits, Data Sampling, Galactic Clusters, Normal Density Functions, Photons, X Ray Telescopes, Astronomy, Mathematics, Computing, Data Processing},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/1979ApJ...228..939C},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Fish1995,
	title        = {Blind deconvolution by means of the Richardson--Lucy algorithm},
	author       = {D. A. Fish and A. M. Brinicombe and E. R. Pike and J. G. Walker},
	year         = 1995,
	month        = {Jan},
	journal      = {J. Opt. Soc. Am. A},
	publisher    = {Optica Publishing Group},
	volume       = 12,
	number       = 1,
	pages        = {58--65},
	doi          = {10.1364/JOSAA.12.000058},
	url          = {http://opg.optica.org/josaa/abstract.cfm?URI=josaa-12-1-58},
	keywords     = {Algorithms; Blind deconvolution; Phase retrieval; Point spread function; Positron emission tomography; Space telescopes},
	abstract     = {A blind deconvolution algorithm based on the Richardson--Lucy deconvolution algorithm is presented. Its performance in the presence of noise is found to be superior to that of other blind deconvolution algorithms. Results are presented and compared with results obtained from implementation of a Weiner filter blind deconvolution algorithm. The algorithm is developed further to incorporate functional forms of the point-spread function with unknown parameters. In the presence of noise the point-spread function can be evaluated with 1.0\% error, and the object can be reconstructed with a quality near that of the deconvolution process with a known point-spread function.}
}
@article{Hogbom1974,
	title        = {{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}},
	author       = {{Hogbom}, J.~A.},
	year         = 1974,
	month        = jun,
	journal      = {Astronomy and Astrophysics Supplement},
	volume       = 15,
	pages        = 417,
	adsurl       = {https://ui.adsabs.harvard.edu/abs/1974A&AS...15..417H},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@incollection{Pytorch2019,
	title        = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
	author       = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
	year         = 2019,
	booktitle    = {Advances in Neural Information Processing Systems 32},
	publisher    = {Curran Associates, Inc.},
	pages        = {8024--8035},
	url          = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf},
	editor       = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}
}
@article{Parameswaran2018,
	title        = {Accelerating {GMM-based} patch priors for image restoration: Three ingredients for a 100$\times$ speed-up},
	author       = {Parameswaran, Shibin and Deledalle, Charles-Alban and Denis, Loic and Nguyen, Truong Q},
	year         = 2018,
	month        = aug,
	journal      = {IEEE Trans. Image Process.},
	publisher    = {Institute of Electrical and Electronics Engineers (IEEE)},
	volume       = 28,
	number       = 2,
	pages        = {687--698},
	copyright    = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	abstract     = {Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems such as denoising, deblurring, super-resolution, inpainting and devignetting. To the best of our knowledge, FEPLL is the first algorithm that can competitively restore a 512$\times$512 pixel image in under 0.5s for all the degradations mentioned above without specialized code optimizations such as CPU parallelization or GPU implementation.},
	language     = {en}
}
@inproceedings{Li2014,
	title        = {Deep Convolutional Neural Network for Image Deconvolution},
	author       = {Xu, Li and Ren, Jimmy SJ and Liu, Ce and Jia, Jiaya},
	year         = 2014,
	booktitle    = {Advances in Neural Information Processing Systems},
	publisher    = {Curran Associates, Inc.},
	volume       = 27,
	pages        = {},
	url          = {https://proceedings.neurips.cc/paper/2014/file/1c1d4df596d01da60385f0bb17a4a9e0-Paper.pdf},
	editor       = {Z. Ghahramani and M. Welling and C. Cortes and N. Lawrence and K.Q. Weinberger}
}
@article{Selig2015,
	title        = {Denoising, deconvolving, and decomposing   photon observations - Derivation of the D3PO algorithm},
	author       = {{Selig, Marco} and {En\ss{}lin, Torsten A.}},
	year         = 2015,
	month        = {},
	journal      = {A\&A},
	volume       = 574,
	pages        = {A74},
	doi          = {10.1051/0004-6361/201323006},
	url          = {https://doi.org/10.1051/0004-6361/201323006}
}
@article{Pumpe2018,
	title        = {Denoising, deconvolving, and decomposing multi-domain photon observations - The D4PO algorithm},
	author       = {{Pumpe, Daniel} and {Reinecke, Martin} and {En\ss{}lin, Torsten A.}},
	year         = 2018,
	journal      = {A\&A},
	volume       = 619,
	pages        = {A119},
	doi          = {10.1051/0004-6361/201832781},
	url          = {https://doi.org/10.1051/0004-6361/201832781}
}
@article{HurleyWalker2022,
	title        = {{GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended (GLEAM-X) I: Survey description and initial data release}},
	author       = {{Hurley-Walker}, N. and {Galvin}, T.~J. and {Duchesne}, S.~W. and {Zhang}, X. and {Morgan}, J. and {Hancock}, P.~J. and {An}, T. and {Franzen}, T.~M.~O. and {Heald}, G. and {Ross}, K. and {Vernstrom}, T. and {Anderson}, G.~E. and {Gaensler}, B.~M. and {Johnston-Hollitt}, M. and {Kaplan}, D.~L. and {Riseley}, C.~J. and {Tingay}, S.~J. and {Walker}, M.},
	year         = 2022,
	month        = aug,
	journal      = {\pasa},
	volume       = 39,
	pages        = {e035},
	doi          = {10.1017/pasa.2022.17},
	keywords     = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
	eid          = {e035},
	archiveprefix = {arXiv},
	eprint       = {2204.12762},
	primaryclass = {astro-ph.GA},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2022PASA...39...35H},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Reeves1995,
	title        = {Generalized cross-validation as a stopping rule for the Richardson-Lucy algorithm},
	author       = {Reeves, Stanley J.},
	year         = 1995,
	journal      = {International Journal of Imaging Systems and Technology},
	volume       = 6,
	number       = 4,
	pages        = {387--391},
	doi          = {https://doi.org/10.1002/ima.1850060412},
	url          = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.1850060412},
	eprint       = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.1850060412},
	abstract     = {Abstract The Richardson-Lucy (R-L) algorithm has been widely used to restore degraded astronomical images. This algorithm is nothing more than the expectation-maximization (EM) algorithm applied to Poisson data. The R-L method is iterative in nature and converges to a (possibly local) maximum of the likelihood function. Unfortunately, because of the ill-conditioned nature of the problem, this maximum likelihood estimate may actually be a very poor restoration. One way to prevent degradation of the restoration is to stop the iteration before it reaches convergence. A number of methods have been proposed for determining the optimal stopping point-the point that provides the best trade-off between restoring the image and amplifying the noise. Cross-validation (CV) has recently been proposed as an advantageous method for determining the optimal stopping point. We propose a different form of CV based on generalized cross-validation (GCV) that overcomes some of the difficulties of CV. We derive a GCV-based criterion for the R-L algorithm that can be efficiently evaluated at each iteration. We present examples displaying the power of the stopping rule and discuss the abilities and shortcomings of the method.}
}
@article{Ulyanov2017,
	title        = {Deep Image Prior},
	author       = {Dmitry Ulyanov and Andrea Vedaldi and Victor S. Lempitsky},
	year         = 2017,
	journal      = {International Journal of Computer Vision},
	volume       = 128,
	pages        = {1867--1888}
}
@article{Altekrueger2022,
	title        = {PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization},
	author       = {Fabian Altekr{\"u}ger and Alexander Denker and Paul Hagemann and Johannes Hertrich and Peter Maass and Gabriele Steidl},
	year         = 2022,
	journal      = {ArXiv},
	volume       = {abs/2205.12021}
}
@article{Papyan2015,
	title        = {Multi-Scale Patch-Based Image Restoration},
	author       = {Papyan, Vardan and Elad, Michael},
	year         = 2016,
	journal      = {IEEE Transactions on Image Processing},
	volume       = 25,
	number       = 1,
	pages        = {249--261},
	doi          = {10.1109/TIP.2015.2499698}
}
@article{Dey2006,
	title        = {Richardson–Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution},
	author       = {Dey, Nicolas and Blanc-Feraud, Laure and Zimmer, Christophe and Roux, Pascal and Kam, Zvi and Olivo-Marin, Jean-Christophe and Zerubia, Josiane},
	year         = 2006,
	journal      = {Microscopy Research and Technique},
	volume       = 69,
	number       = 4,
	pages        = {260--266},
	doi          = {https://doi.org/10.1002/jemt.20294},
	url          = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/jemt.20294},
	keywords     = {image deconvolution, total variation regularization, Poisson noise, fluorescence confocal microscopy},
	eprint       = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/jemt.20294},
	abstract     = {Abstract Confocal laser scanning microscopy is a powerful and popular technique for 3D imaging of biological specimens. Although confocal microscopy images are much sharper than standard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson–Lucy iterative algorithm, which computes maximum likelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regularization constraints based on some prior knowledge on the data have to be applied to stabilize the solution. Here, we propose to combine the Richardson–Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges. We show on simulated and real images that this constraint improves the deconvolution results as compared with the unregularized Richardson–Lucy algorithm, both visually and quantitatively. Microsc. Res. Tech. 69:260–266, 2006. © 2006 Wiley-Liss, Inc.}
}
@article{Bi1994,
	title        = {{When does the Richardson-Lucy deconvolution converge?}},
	author       = {{Bi}, H. and {Boerner}, G.},
	year         = 1994,
	month        = dec,
	journal      = {\aaps},
	volume       = 108,
	pages        = {409--415},
	keywords     = {METHODS: DATA ANALYSIS, METHODS: STATISTICAL, TECHNIQUES: IMAGE PROCESSING},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/1994A&AS..108..409B},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@software{Gammapyv1.0,
	title        = {Gammapy: Python toolbox for gamma-ray astronomy},
	author       = {Acero, Fabio and Aguasca-Cabot, Arnau and Buchner, Johannes and Carreto Fidalgo, David and Chen, Andrew and Chromey, Alisha and Contreras Gonzalez, José Luis and de Bony de Lavergne, Mathieu and de Miranda Cardoso, José Vinícius and Deil, Christoph and Donath, Axel and Giunti, Luca and Hinton, James and Jouvin, Léa and Khélifi, Bruno and King, Johannes and Lefaucheur, Julien and Lenain, Jean-Philippe and Linhoff, Maximilian and López-Coto, Rubén and Mohrmann, Lars and Morcuende, Daniel and Nakashima, Kaori and Nigro, Cosimo and Olivera-Nieto, Laura and Owen, Ellis and Panny, Sebastian and Papadopoulos Orfanos, Dimitri and Paz Arribas, Manuel and Pintore, Fabio and Poon, Helen and Remy, Quentin and Ruiz, José Enrique and Siejkowski, Hubert and Sinha, Atreyee and Sipőcz, Brigitta M and Spir-Jacob, Marion and Terrier, Régis and Tibaldo, Luigi and Unbehaun, Tim and van Eldik, Christopher and Vuillaume, Thomas and Weinstein, Amanda and Wood, Matthew},
	year         = 2022,
	month        = nov,
	publisher    = {Zenodo},
	doi          = {10.5281/zenodo.7311399},
	url          = {https://doi.org/10.5281/zenodo.7311399},
	note         = {{If you use this software, please cite it using the metadata from this file.}},
	version      = {1.0}
}
@inproceedings{Rosenbaum15,
	title        = {The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors},
	author       = {Rosenbaum, Dan and Weiss, Yair},
	year         = 2015,
	booktitle    = {Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2},
	location     = {Montreal, Canada},
	publisher    = {MIT Press},
	address      = {Cambridge, MA, USA},
	series       = {NIPS'15},
	pages        = {2683–2691},
	abstract     = {In recent years, approaches based on machine learning have achieved state-of-the-art performance on image restoration problems. Successful approaches include both generative models of natural images as well as discriminative training of deep neural networks. Discriminative training of feed forward architectures allows explicit control over the computational cost of performing restoration and therefore often leads to better performance at the same cost at run time. In contrast, generative models have the advantage that they can be trained once and then adapted to any image restoration task by a simple use of Bayes' rule.In this paper we show how to combine the strengths of both approaches by training a discriminative, feed-forward architecture to predict the state of latent variables in a generative model of natural images. We apply this idea to the very successful Gaussian Mixture Model (GMM) of natural images. We show that it is possible to achieve comparable performance as the original GMM but with two orders of magnitude improvement in run time while maintaining the advantage of generative models.},
	numpages     = 9
}
@article{Akhaury2022,
	title        = {Deep learning-based galaxy image deconvolution},
	author       = {Akhaury, Utsav and Starck, Jean-Luc and Jablonka, Pascale and Courbin, Frédéric and Michalewicz, Kevin},
	year         = 2022,
	journal      = {Frontiers in Astronomy and Space Sciences},
	volume       = 9,
	doi          = {10.3389/fspas.2022.1001043},
	issn         = {2296-987X},
	url          = {https://www.frontiersin.org/articles/10.3389/fspas.2022.1001043},
	abstract     = {With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution. We propose a new deconvolution method based on the Learnlet transform. Eventually, we investigate and compare the performance of different Unet architectures and Learnlet for image deconvolution in the astrophysical domain by following a two-step approach: a Tikhonov deconvolution with a closed-form solution, followed by post-processing with a neural network. To generate our training dataset, we extract HST cutouts from the CANDELS survey in the F606W filter (V-band) and corrupt these images to simulate their blurred-noisy versions. Our numerical results based on these simulations show a detailed comparison between the considered methods for different noise levels.}
}
@inproceedings{Donath2022,
	title        = {{P}ylira: deconvolution of images in the presence of {P}oisson noise},
	author       = {{D}onath, {A}xel and {S}iemiginowska, {A}neta and {K}ashyap, {V}inay  and  {B}urke, {D}ouglas and {R}eddy {S}olipuram, {K}arthik and van {D}yk, {D}avid},
	year         = 2022,
	booktitle    = {{P}roceedings of the 21st {P}ython in {S}cience {C}onference},
	pages        = {98--104},
	doi          = {10.25080/majora-212e5952-00f},
	editor       = {{M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe}
}
@article{Schawinski2017,
	title        = {{Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit}},
	author       = {Schawinski, Kevin and Zhang, Ce and Zhang, Hantian and Fowler, Lucas and Santhanam, Gokula Krishnan},
	year         = 2017,
	month        = {01},
	journal      = {Monthly Notices of the Royal Astronomical Society: Letters},
	volume       = 467,
	number       = 1,
	pages        = {L110-L114},
	doi          = {10.1093/mnrasl/slx008},
	issn         = {1745-3925},
	url          = {https://doi.org/10.1093/mnrasl/slx008},
	abstract     = {{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 \\&lt; z \\&lt; 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.}},
	eprint       = {https://academic.oup.com/mnrasl/article-pdf/467/1/L110/10730451/slx008.pdf}
}
@article{VanDenOord2014,
	title        = {The Student-t Mixture as a Natural Image Patch Prior with Application to Image Compression},
	author       = {A{\"a}ron van den Oord and Benjamin Schrauwen},
	year         = 2014,
	journal      = {Journal of Machine Learning Research},
	volume       = 15,
	number       = 60,
	pages        = {2061--2086},
	url          = {http://jmlr.org/papers/v15/vandenoord14a.html}
}
@software{GammapyZenodov1.0.1,
	title        = {Gammapy: Python toolbox for gamma-ray astronomy},
	author       = {Acero, Fabio and Aguasca-Cabot, Arnau and Buchner, Johannes and Carreto Fidalgo, David and Chen, Andrew and Chromey, Alisha and Contreras Gonzalez, José Luis and de Bony de Lavergne, Mathieu and de Miranda Cardoso, José Vinícius and Deil, Christoph and Donath, Axel and Giunti, Luca and Hinton, James and Jouvin, Léa and Khélifi, Bruno and King, Johannes and Lefaucheur, Julien and Lenain, Jean-Philippe and Linhoff, Maximilian and López-Coto, Rubén and Mohrmann, Lars and Morcuende, Daniel and Nakashima, Kaori and Nigro, Cosimo and Olivera-Nieto, Laura and Owen, Ellis and Panny, Sebastian and Papadopoulos Orfanos, Dimitri and Paz Arribas, Manuel and Pintore, Fabio and Poon, Helen and Remy, Quentin and Ruiz, José Enrique and Siejkowski, Hubert and Sinha, Atreyee and Sipőcz, Brigitta M and Spir-Jacob, Marion and Terrier, Régis and Tibaldo, Luigi and Unbehaun, Tim and van Eldik, Christopher and Vuillaume, Thomas and Weinstein, Amanda and Wood, Matthew},
	year         = 2023,
	month        = mar,
	publisher    = {Zenodo},
	doi          = {10.5281/zenodo.7734804},
	url          = {https://doi.org/10.5281/zenodo.7734804},
	note         = {{If you use this software, please cite it using the metadata from this file.}},
	version      = {v1.0.1}
}
@inproceedings{Kingma2014,
	title        = {{Adam: A Method for Stochastic Optimization}},
	author       = {Kingma, Diederik P. and Ba, Jimmy},
	year         = 2014,
	month        = 12,
	eprint       = {1412.6980},
	archiveprefix = {arXiv},
	primaryclass = {cs.LG}
}
@article{Gammapy2023,
	title        = {Gammapy: A Python package for gamma-ray astronomy},
	author       = {{Donath}, Axel and {Terrier, R\'egis} and {Remy, Quentin} and {Sinha, Atreyee} and {Nigro, Cosimo} and {Pintore, Fabio} and {Kh\'elifi, Bruno} and {Olivera-Nieto, Laura} and {Ruiz, Jose Enrique} and {Br\"ugge, Kai} and {Linhoff, Maximilian} and {Contreras, Jose Luis} and {Acero, Fabio} and {Aguasca-Cabot, Arnau} and {Berge, David} and {Bhattacharjee, Pooja} and {Buchner, Johannes} and {Boisson, Catherine} and {Carreto Fidalgo, David} and {Chen, Andrew} and {de Bony de Lavergne, Mathieu} and {de Miranda Cardoso, Jos\'e Vinicius} and {Deil, Christoph} and {F\"u\ss{}ling, Matthias} and {Funk, Stefan} and {Giunti, Luca} and {Hinton, Jim} and {Jouvin, L\'ea} and {King, Johannes} and {Lefaucheur, Julien} and {Lemoine-Goumard, Marianne} and {Lenain, Jean-Philippe} and {L\'opez-Coto, Rub\'en} and {Mohrmann, Lars} and {Morcuende, Daniel} and {Panny, Sebastian} and {Regeard, Maxime} and {Saha, Lab} and {Siejkowski, Hubert} and {Siemiginowska, Aneta} and {Sip"ocz, Brigitta M.} and {Unbehaun, Tim} and {van Eldik, Christopher} and {Vuillaume, Thomas} and {Zanin, Roberta}},
	year         = 2023,
	journal      = {A\&amp;A},
	volume       = 678,
	pages        = {A157},
	doi          = {10.1051/0004-6361/202346488},
	url          = {https://doi.org/10.1051/0004-6361/202346488}
}
@misc{Fermitools2019,
	title        = {{Fermitools: Fermi Science Tools}},
	author       = {{Fermi Science Support Development Team}},
	year         = 2019,
	month        = may,
	pages        = {ascl:1905.011},
	keywords     = {Software, NASA, Fermi},
	howpublished = {Astrophysics Source Code Library, record ascl:1905.011},
	eid          = {ascl:1905.011},
	archiveprefix = {ascl},
	eprint       = {1905.011},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2019ascl.soft05011F},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
}
