  Simultaneous sparse approximation is a generalization of the standard sparse
approximation, for simultaneously representing a set of signals using a common
sparsity model. Generalizing the compressive sensing concept to the
simultaneous sparse approximation yields distributed compressive sensing (DCS).
DCS finds the sparse representation of multiple correlated signals using the
common + innovation signal model. However, DCS is not efficient for joint
recovery of a large number of signals since it requires large memory and
computational time. In this paper, we propose a new hierarchical algorithm to
implement the jointly sparse recovery framework of DCS more efficiently. The
proposed algorithm is applied to video background extraction problem, where the
background corresponds to the common sparse activity across frames.
