  We present a Compressive Sensing algorithm for reconstructing binary signals
from its linear measurements. The proposed algorithm minimizes a non-convex
cost function expressed as a weighted sum of smoothed $\ell_0$ norms which
takes into account the binariness of signals. We show that for binary signals
the proposed algorithm outperforms other existing algorithms in recovery rate
while requiring a short run time.
