Measuring the shape information of a galaxy is essential for certain astrophysical domains, such Weak Gravitational Lensing and Galaxy Evolution. In this work, we developed a Shape Constraint which allows to restore galaxy images with lower errors and more robustness for the ellipticity estimation. The constraint was designed in a plug-and-play approach and can be added to any algorithm minimizing a loss function. The code related to this work is available here.

This work has been performed by Fadi Nammour, Morgan A. Schmitz, Fred Maurice Ngolè Mboula, Jean-Luc Starck and Julien N. Girard.

Sparse recovery allows us to reconstruct radio-interferometric images with a resolution increased by a factor two compared to the CLEAN method. This has been confirmed by comparing two images of the Cygnus A radio source, the first one from the LOFAR instrument and reconstructed using sparsity, and the second one from the Very Large Array at a higher frequency (and therefore with a better resolution). Click here to access the original paper.

This work has been done by Hugh Garsden, Julien N. Girard, Jean-Luc Starck et al.

The Point Spread Function (PSF) describes how an imaging system responds to an unresolved point source. Astrophysicists can treat distant stars like point sources in order to get a measure of the PSF at different locations. Unfortunately, in some cases, stars will be too distant or insufficient in number and will not provide a measure of the PSF at the exact location of galaxies, which are the primary objects of interest. To tackle these problems, we developed new methods for the super-resolution and interpolation of measured PSFs. Additionally, we developed  deconvolution techniques for galaxy images when the PSF is fully or only partially known.

The main contributors on this topic are Fred Maurice Ngolè Mboula, Samuel Farrens and Jean-Luc Starck. For more details, click here.