Orateur
Fadi Nammour
(CosmoStat, CEA Paris-Saclay)
Description
Telescope images are corrupted with blur and noise. Generally, blur is represented by a convolution with a Point Spread Function and noise is modelled as Additive Gaussian Noise. Restoring galaxy images from the observations is an inverse problem that is ill-posed and specifically ill-conditioned. The majority of the standard reconstruction methods minimise the Mean Square Error to reconstruct images, without any guarantee that the shape objects contained in the data (e.g. galaxies) is preserved. Here we introduce a shape constraint, exhibit its properties and show how it preserves galaxy shapes when combined to Machine Learning reconstruction algorithms.
Auteurs principaux
Fadi Nammour
(CosmoStat, CEA Paris-Saclay)
François LANUSSE
({CNRS}UMR7158)
Julien Girard
(AIM/P7)
Florent Sureau
(CEA Paris-Saclay)
Jean-Luc Starck
(CosmoStat, CEA Paris-Saclay)
