Compressed sensing recovery techniques allow for reconstruction of an unknown sparse vector from an underdetermined system of linear equations. Recently, a lot of attention was drawn to the problem of recovering the sparse vector from quantized CS measurements. Especially interesting is the case, when extreme quantization is enforced that captures only the sign of the measurements. The problem becomes even more difficult if the measurements are corrupted by noise. In this paper we consider \ac{AWGN}. To solve this problem, we employ the highly efficient \ac{GAMP} algorithm and provide closed-form expressions for the nonlinear steps. We demonstrate superiority of this approach in terms of the \ac{MSE}-performance compared to a similar state-of-the-art algorithm from the literature.