Reconstruction of block-sparse signals by using an l2/p-regularized least-squares algorithm

A new algorithm for the reconstruction of so called block-sparse signals in a compressive sensing framework is presented. The algorithm is based on minimizing an ℓ_2/p-norm regularized l₂ error. The minimization is carried out by using a sequential conjugate-gradient algorithm where the line search involved is carried out using a technique based on Banach's fixed-point theorem. Simulation results are presented which show that for large-size data the proposed algorithm yields improved reconstruction performance and requires a reduced amount of computation relative to several known algorithms.