We focus on the exact resolution of sparse spectral unmixing problems, that is, the search for cardinality-limited linear least squares solutions under non-negativity and sum-to-one constraints. The originality of the proposed method - for which the Python code is provided - lies in its multisolution nature; we return the set of supports that yield the best solutions. The method is tested on synthetic data, with promising results.
We propose an algorithm that exactly solves the cardinality-constrained sparse spectral unmixing problem.
Dans cette contribution, nous abordons le problème de reconstruction d’image de distribution radioactive pour lequel l’information disponible provient de plusieurs classes de données distinctes, chacune associée à une combinaison spécifique de détections.
Our contribution focuses at improving the image reconstruction process for specific Compton imaging systems able to detect multiple classes of events, in the field of nuclear imaging.