  We propose a new method, {\it binary fused compressive sensing} (BFCS), to
recover sparse piece-wise smooth signals from 1-bit compressive measurements.
The proposed algorithm is a modification of the previous {\it binary iterative
hard thresholding} (BIHT) algorithm, where, in addition to the sparsity
constraint, the total-variation of the recovered signal is upper constrained.
As in BIHT, the data term of the objective function is an one-sided $\ell_1$
(or $\ell_2$) norm. Experiments on the recovery of sparse piece-wise smooth
signals show that the proposed algorithm is able to take advantage of the
piece-wise smoothness of the original signal, achieving more accurate recovery
than BIHT.
