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1 SEDI - Service d-Electronique, des Détecteurs et d-Informatique 2 Equipe Image - Laboratoire GREYC - UMR6072 GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen

Abstract : The matter density is an important knowledge for today cosmology as many phenomena are linked to matter fluctuations. However, this density is not directly available, but estimated through lensing maps or galaxy surveys. In this article, we focus on galaxy surveys which are incomplete and noisy observations of the galaxy density. Incomplete, as part of the sky is unobserved or unreliable. Noisy as they are count maps degraded by Poisson noise. Using a data augmentation method, we propose a two-step method for recovering the density map, one step for inferring missing data and one for estimating of the density. The results show that the missing areas are efficiently inferred and the statistical properties of the maps are very well preserved.

Keywords : Inpainting Bayesian framework Sparse representation Poisson noise Data augmentation





Author: François-Xavier Dupé - Jalal M. Fadili - Jean-Luc Starck -

Source: https://hal.archives-ouvertes.fr/



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