Blind source separation :

The team work on sparse blind source separation has led to the implementation the following softwares:

GMCALab  implements a simple but yet effective sparse BSS method (in Matlab)

pyGMCALab  implements GMCA and AMCA (in Python)

nGMCALab extension of the GMCA algorithm for Non-negative Matrix Factorization (in Matlab and Python)

Component separation and Planck data processing :

LGMCA  is a sparse component separation based on GMCA that is specifically tailored for the estimation of the CMB map from full-sky microwave surveys (WMAP and Planck).

Algorithm for solving sparse regression in signal processing :

Nesta is a sparse regression solver based on proximal algorithms and multi-step techniques in optimization.

pRestoreManifold  used for restoration problems on Manifolds (in Python and C++)