  We consider compressed sensing of block-sparse signals, i.e., sparse signals
that have nonzero coefficients occuring in clusters. Based on an uncertainty
relation for block-sparse signals, we define a block-coherence measure and we
show that a block-version of the orthogonal matching pursuit algorithm recovers
block k-sparse signals in no more than k steps if the block-coherence is
sufficiently small. The same condition on block-sparsity is shown to guarantee
successful recovery through a mixed l2/l1 optimization approach. The
significance of the results lies in the fact that making explicit use of
block-sparsity can yield better reconstruction properties than treating the
signal as being sparse in the conventional sense thereby ignoring the
additional structure in the problem.
