This chapter focuses on solving an under-determined linear inverse problem by co-sparse recovery. It presents different techniques to solve for the co-sparse analysis prior formulation. The chapter illustrates the Split Bregman technique in solving an unconstrained problem. The basic idea in algorithms such as orthogonal matching pursuit is that the support of the sparse signal is estimated iteratively, from which the values at those non-zero positions are obtained. In greedy analysis prior algorithm, positions that do not satisfy the constraints are progressively removed. This is known as identifying the co-support, that is, position of zero values in the co-sparse signal.