  We propose a blind deconvolution method for signals on graphs, with the exact
sparseness constraint for the original signal. Graph blind deconvolution is an
algorithm for estimating the original signal on a graph from a set of blurred
and noisy measurements. Imposing a constraint on the number of nonzero elements
is desirable for many different applications. This paper deals with the problem
with constraints placed on the exact number of original sources, which is given
by an optimization problem with an $\ell_0$ norm constraint. We solve this
non-convex optimization problem using the ADMM iterative solver. Numerical
experiments using synthetic signals demonstrate the effectiveness of the
proposed method.
