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Home All issues Volume 643 (November 2020) A&A, 643 (2020) A43 References
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Issue
A&A
Volume 643, November 2020
Article Number A43
Number of page(s) 29
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201936278
Published online 29 October 2020
  1. Ackermann, M., Ajello, M., Allafort, A., et al. 2013, ApJS, 209, 34 [NASA ADS] [CrossRef] [Google Scholar]
  2. Aujol, J.-F., Gilboa, G., Chan, T., & Osher, S. 2006, Int. J. Comput. Vision, 67, 111 [CrossRef] [Google Scholar]
  3. Banterle, F., Corsini, M., Cignoni, P., & Scopigno, R. 2012, Comput. Graphics Forum, 31, 19 [CrossRef] [Google Scholar]
  4. Barchi, P., de Carvalho, R., Rosa, R., et al. 2020, Astron. Comput., 30, 100334 [CrossRef] [Google Scholar]
  5. Beckouche, S., Starck, J.-L., & Fadili, J. 2013, A&A, 556, A132 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bertero, M., & Boccacci, P. 1998, Introduction to Inverse Problems in Imaging (CRC Press) [CrossRef] [Google Scholar]
  7. Bertero, M., & Piana, M. 2006 Inverse problems in biomedical imaging: modeling and methods of solution, eds. A. Quarteroni, L. Formaggia, & A. Veneziani (Milano: Springer Milan), 1 [Google Scholar]
  8. Bertin, E. 2009, Mem. Soc. Astron. It., 80, 422 [NASA ADS] [Google Scholar]
  9. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Black, M. J., Sapiro, G., Marimont, D. H., & Heeger, D. 1998, IEEE Trans. Image Process., 7, 421 [CrossRef] [Google Scholar]
  11. Buades, A., Coll, B., & Morel, J. M. 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2, 60 [CrossRef] [Google Scholar]
  12. Buades, A., Coll, B., & Morel, J.-M. 2011, Image Process. Line, 1, 208 [Google Scholar]
  13. Bush, J. 2011, Master’s thesis, University of California, Santa Barbara, http://www.math.ucsb.edu/ cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf [Google Scholar]
  14. Castellano, M., Ottaviani, D., Fontana, A., et al. 2015, in Astronomical Data Analysis Software and Systems XXIV (ADASS XXIV), eds. A. R. Taylor, & E. Rosolowsky, ASP Conf. Ser., 495, 257 [Google Scholar]
  15. Castellano, M., Fontana, A., Boutsia, K., et al. 2010, A&A, 511, A20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Chambolle, A. 2004, J. Math. Imaging Vision, 20, 89 [CrossRef] [Google Scholar]
  17. Chang, S. G., Yu, B., & Vetterli, M. 2000, IEEE Trans. Image Process., 9, 1532 [NASA ADS] [CrossRef] [Google Scholar]
  18. Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. 2007, IEEE Trans. Image Process., 16, 2080 [Google Scholar]
  19. Darbon, J., Cunha, A., Chan, T. F., Osher, S., & Jensen, G. J. 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1331 [CrossRef] [Google Scholar]
  20. Dark Energy Survey Collaboration (Abbott, T., et al.), 2016, MNRAS, 460, 1270 [NASA ADS] [CrossRef] [Google Scholar]
  21. Donoho, D. L., & Johnstone, J. M. 1994, Biometrika, 81, 425 [CrossRef] [MathSciNet] [Google Scholar]
  22. Fontana, A., Dunlop, J. S., Paris, D., et al. 2014, A&A, 570, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Frontera-Pons, J., Sureau, F., Bobin, J., & Le Floc’h, E. 2017, A&A, 603, A60 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Gabor, D. 1965, Lab. Invest., 14, 801 [Google Scholar]
  25. Getreuer, P. 2012, Image Process. Line, 2, 74 [CrossRef] [Google Scholar]
  26. Goldstein, T., & Osher, S. 2009, SIAM J. Imaging Sci., 2, 323 [CrossRef] [Google Scholar]
  27. Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35 [NASA ADS] [CrossRef] [Google Scholar]
  28. Guo, Z., Sun, J., Zhang, D., & Wu, B. 2012, IEEE Trans. Image Process., 21, 958 [CrossRef] [Google Scholar]
  29. Guo, Y., Ferguson, H. C., Giavalisco, M., et al. 2013, ApJS, 207, 24 [NASA ADS] [CrossRef] [Google Scholar]
  30. Jones, E., Oliphant, T., Peterson, P., et al. 2001, SciPy: Open source scientific tools for Python [Google Scholar]
  31. Karmakar, A., Mishra, D., & Tej, A., 2018, ArXiv e-prints [arXiv:1809.01434] [Google Scholar]
  32. Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Selim, I. M. 2017, ArXiv e-prints [arXiv:1709.02245] [Google Scholar]
  33. Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36 [NASA ADS] [CrossRef] [Google Scholar]
  34. Kolmogorov, A. M., & Fomin, S. V. 1957, Elements of the Theory of Functions and Functional Analysis (Dover Publications) [Google Scholar]
  35. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv e-prints [arXiv:1110.3193] [Google Scholar]
  36. Lee, G. R., Gommers, R., Waselewski, F., Wohlfahrt, K., & O’Leary, A. 2019, J. Open Sour. Softw., 4, 1237 [CrossRef] [Google Scholar]
  37. Lesser, M. 2015, PASP, 127, 1097 [CrossRef] [Google Scholar]
  38. Lindenbaum, M., Fischer, M., & Bruckstein, A. 1994, Pattern Recogn., 27, 1 [CrossRef] [Google Scholar]
  39. LSST Science Collaboration (Abell, P. A., et al.) 2009, ArXiv e-prints [arXiv:0912.0201] [Google Scholar]
  40. Mallat, S. 2001, IEEE Trans. Pattern Anal. Mach. Intell., 11, 674 [NASA ADS] [CrossRef] [Google Scholar]
  41. Meyer, Y. 1990, Ondelettes et opérateurs: Ondelettes (Hermann) [Google Scholar]
  42. Neyman, J., & Pearson, E. S. 1933, Phil. Trans. R. Soc. A: Math., Phys. Eng. Sci., 231, 289337 [Google Scholar]
  43. Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713 [NASA ADS] [CrossRef] [Google Scholar]
  44. Perona, P., & Malik, J. 1990, IEEE Trans. Pattern Anal. Mach. Intell., 12, 629 [CrossRef] [Google Scholar]
  45. Peyré, G., Fadili, J., & Starck, J.-L. 2010, SIAM J. Imaging Sci., 3, 646 [CrossRef] [Google Scholar]
  46. Pierre, M., Valtchanov, I., Altieri, B., et al. 2004, JCAP, 2004, 011 [CrossRef] [Google Scholar]
  47. Principe, G., Malyshev, D., Ballet, J., & Funk, S. 2018, A&A, 618, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Romeo, A. B., Horellou, C., & Bergh, J. 2003, MNRAS, 342, 337 [NASA ADS] [CrossRef] [Google Scholar]
  49. Rudin, L. I., Osher, S., & Fatemi, E. 1992, Phys. D: Nonlinear Phenom., 60, 259 [CrossRef] [Google Scholar]
  50. Schreiber, C., Elbaz, D., Pannella, M., et al. 2017, A&A, 602, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Shen, H., George, D., Huerta, E. A., & Zhao, Z. 2017, ArXiv e-prints [arXiv:1711.09919] [Google Scholar]
  52. Shensa, M. J. 1992, IEEE Trans. Signal Process., 40, 2464 [NASA ADS] [CrossRef] [Google Scholar]
  53. Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24 [Google Scholar]
  54. Spergel, D., Gehrels, N., Baltay, C., et al. 2015, ArXiv e-prints [arXiv:1503.03757] [Google Scholar]
  55. Starck, J. L., Aghanim, N., & Forni, O. 2004, A&A, 416, 9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Starck, J. L., Moudden, Y., Abrial, P., & Nguyen, M. 2006, A&A, 446, 1191 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Starck, J.-L., Murtagh, F., & Fadili, J. 2010, Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis (Cambridge University Press) [Google Scholar]
  58. Starck, J.-L., Murtagh, F., & Bertero, M. 2014, Starlet Transform Astron. Data Process., 1 [Google Scholar]
  59. Tomasi, C., & Manduchi, R. 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 839 [CrossRef] [Google Scholar]
  60. Tuccillo, D., Huertas-Company, M., Decenciére, E., & Velasco-Forero, S. 2017 Proc. International Astronomical Union, IAU Symp., 325, 191 [Google Scholar]
  61. Valens, C. 1999, A Really Friendly Guide to Wavelets [Google Scholar]
  62. van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., et al. 2014, PeerJ, 2, e453 [Google Scholar]
  63. Vetterli, M., & Kovačevic, J. 1995, Wavelets and Subband Coding (Upper Saddle River, NJ, USA: Prentice-Hall, Inc.) [Google Scholar]
  64. Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. 2008, Proceedings of the 25th International Conference on Machine Learning, ICML ’08 (New York, NY, USA: Association for Computing Machinery), 1096 [Google Scholar]
  65. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P.-A. 2010, J. Mach. Learn. Res., 11, 3371 [Google Scholar]
  66. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. 2004, IEEE Trans. Image Process., 13, 600 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  67. Weickert, J. 1998, Anisotropic Diffusion in Image Processing (Teubner) [Google Scholar]
  68. Winkler, A. M. 2011, Gaussian kernels: convert FWHM to sigma [Google Scholar]
  69. Xie, J., Xu, L., & Chen, E. 2012, in Advances in Neural Information Processing Systems, eds. F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Curran Associates, Inc.), 25, 341 [Google Scholar]

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