Hybrid greedy pursuit

For constructing the support set of a sparse vector in the standard compressive sensing framework, we develop a hybrid greedy pursuit algorithm that combines the advantages of serial and parallel atom selection strategies. In an iterative framework, the hybrid algorithm uses a joint sparsity information extracted from the independent use of serial and parallel greedy pursuit algorithms. Through experimental evaluations, the hybrid algorithm is shown to provide a significant improvement for the support set recovery performance.