Blind sparsity weak subspace pursuit for compressed sensing

A type of back-track algorithm has recently been proposed to handle sparse reconstruction problems arising in compressed sensing. This sort of algorithm is attractive owing to its recovery performance; however, it requires the sparsity level to be known a priori. A blind sparsity algorithm is presented based on subspace pursuit and weak pruning, and its convergence is demonstrated. The experiments validate the proposed algorithm´s superior performance to that of several other back-tracking-type and optimisation methods.