Block sparsity adaptive iteration algorithm for compressed sensing

Block-sparse signal is a typical sparse signal. Among the block-sparse signal problems for compressed sensing, the most existing recovery algorithms require block sparsity as prior knowledge and have a high complexity. In this paper, a block sparsity adaptive iteration algorithm for compressed sensing has been proposed when the block sparsity is unknown. Firstly, the algorithm initializes a block sparsity which will increase by steps. Subsequently, for each block sparsity, a sub-set of the signal support set can be determined by the algorithm, which updates the previous one, until the exact support set is acquired, finally the original signal can be reconstructed through the exact support set. This algorithm doesn't require block sparsity as prior knowledge and has a low complexity. Simulation results demonstrate its high recovery probability than most existing algorithms, which makes it a promising for practical block-sparse signal compressed sensing task.