  Blind source separation (BSS) aims at recovering signals from mixtures. This
problem has been extensively studied in cases where the mixtures are
contaminated with additive Gaussian noise. However, it is not well suited to
describe data that are corrupted with Poisson measurements such as in low
photon count optics or in high-energy astronomical imaging (e.g. observations
from the Chandra or Fermi telescopes). To that purpose, we propose a novel BSS
algorithm coined pGMCA that specifically tackles the blind separation of sparse
sources from Poisson measurements.
