A reconstruction algorithm for compressed sensing based on improved quantum-behaved particle swarm optimization algorithm and LP norm

Currently, the research of compressed sensing (CS) mainly focuses on reconstruction algorithm, the accuracy and speed of which largely determines the performance of CS. In this paper, particle swarm optimization algorithm (PSO) is applied to the compressed sensing reconstruction. As the reconstruction algorithms based on L1-minimizing need too much sampling data, this paper transforms the reconstruction model for CS into the Lp-minimization model, and takes Lp-minimizing as the optimization goal. Then an improved QPSO-based (quantum-behaved particle swarm optimization) CS reconstruction algorithm is proposed, which has the advantages of fast convergence and good global search capability. Numerical experiments show that the proposed algorithm has a good reconstruction quality for sparse signals.