Blind Source Separation with Outliers in Transformed Domains

International audienceBlind Source Separation (BSS) methods are well suited for the analysis of multichannel data. In many applications, the observations are corrupted by an additional structured noise, which hinders most of the standard BSS techniques. In this article, we propose a novel BSS method able to jointly unmix the sources and separate the source contribution from the structured noise or outliers. This separation builds upon the difference of morphology between the components of interest, often encountered in imaging problems, by exploiting a sparse modeling of the components in two different domains. Numerical experiments highlight the robustness and precision of the proposed method in a wide variety of settings, including the full-rank regime