Recovery Of Block Sparse Signals Using The Framework Of Block Sparse Bayesian Learning

In this paper we study the recovery of block sparse signals and extend conventional approaches in two important directions; one is learning and exploiting intra-block correlation, and the other is generalizing signals' block structure such that the block partition is not needed to be known for recovery. We propose two algorithms based on the framework of block sparse Bayesian learning (bSBL). One algorithm, directly derived from the framework, requires a priori knowledge of the block partition. Another algorithm, derived from an expanded bSBL framework using the generalization method, can be used when the block partition is unknown. Experiments show that they have superior performance to state-of-the-art algorithms.