LENA (non-LinEar sigNal processing for solving data challenges in Astrophysics) is a research project as well as a team financed by the ERC Starting Grant program.

The goal of this project is to develop the next generation models and restoration methods for solving inverse problems in signal and image processing. These models and methods will take their roots in recent advances in applied mathematics: sparse signal modelling, proximal algorithms and machine learning. They will allow extending sparse models and methods to the non-linear world. These developments will further provide a bridge between signal processing  and machine learning providing new approaches to model and restore signal and image beyond the standard linear methods.

These algorithms will be deployed in the field of astrophysics to tackle key image processing challenges arising in missions including Planck, Euclid and radio-interferometry.

More information about this project can be find at this location.