# Sparse component separation from Poisson measurements

## TL;DR

This paper introduces pGMCA, a novel blind source separation algorithm designed specifically for sparse signals contaminated with Poisson noise, addressing challenges in low photon count and high-energy astronomical imaging.

## Contribution

The paper presents a new BSS method tailored for Poisson measurements, filling a gap in existing Gaussian noise-based approaches.

## Key findings

- Effective separation of sparse sources from Poisson data.
- Applicable to low photon count and astronomical imaging.
- Demonstrates improved performance over traditional methods.

## Abstract

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.