  The performance of sparse signal recovery from noise corrupted,
underdetermined measurements can be improved if both sparsity and correlation
structure of signals are exploited. One typical correlation structure is the
intra-block correlation in block sparse signals. To exploit this structure, a
framework, called block sparse Bayesian learning (BSBL), has been proposed
recently. Algorithms derived from this framework showed superior performance
but they are not very fast, which limits their applications. This work derives
an efficient algorithm from this framework, using a marginalized likelihood
maximization method. Compared to existing BSBL algorithms, it has close
recovery performance but is much faster. Therefore, it is more suitable for
large scale datasets and applications requiring real-time implementation.
