The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse
signals and we propose a novel formulation, based on polynomial
optimization. This approach is analyzed and compared to the
state-of-the-art binary compressed sensing methods.