Split bregman algorithms for joint sparse recovery with analysis prior

Joint sparse recovery problem simultaneously recover a set of jointly sparse signals from underdetermined linear measurements. This problem is an extension of single measurement vector recovery in compressed sensing, but is generally considered to be difficult due to the mixed-norm structure. In this paper, we propose robust and efficient algorithms based on split Bregman iteration to solve the joint sparse recovery problems with analysis prior. The proposed algorithms has low computational complexity and are suitable for large scale problems. Numerical results show the effectiveness of the proposed algorithms.