Accurate signal recovery in quantized compressed sensing

Compressed sensing (CS) studies the recovery of a high dimensional signal from its low dimensional linear measurements under a sparsity prior. This paper is focused on the CS problem with quantized measurements. An algorithm is proposed based on a Bayesian perspective that treats measurement noises and quantization errors separately and allows data saturation. It is shown to improve the recovery accuracy in comparison with existing approaches by numerical simulations.