Exploiting astronomic data requires a proper handling of the various instrumental effects. These effects include light diffraction, pixelization, instrumental noise etc.
An important line of work under development in the CosmoStat is focused on characterizing an optical instrument Point Spread Function (PSF), based on unresolved objects images. This activity aims at proposing robust model-free PSFs estimation methods, dealing in particular with images undersampling and PSFs intrinsic variability.
In the continuity of the PSF modeling researches, we seek for galaxies images restoration methods that are suitable for quantitative measurements analysis, in a cosmological framework. This implies in particular preserving the galaxies shapes at best. As for the PSFs, we investigate non-parametric methods.
Publications
- F. M. Ngolè Mboula, J.-L. Starck, S. Ronayette,K. Okomura, J. Amiaux. Super-resolution method using sparse regularization for point spread function recovery,Astronomy and Astrophysics, 2014. Available here.
- F. M. Ngolè Mboula, J.-L. Starck, K. Okomura, J. Amiaux, P. Hudelot. Constraint matrix factorization for space variant PSFs field restoration, Inverse Problems. Available here.
- F. M. Ngolè Mboula, J.-L. Starck. PSFs field learning based on Optimal Transport distances, Submitted.
- S. Farrens, F. M. Ngolè Mboula, J.-L. Starck. Space variant deconvolution of galaxy survey images, Submitted.