Image Sparse Decomposition Based on Particle Swarm Optimization with Chaotic Mutation

A matching pursuit algorithm based on an improved particle swarm optimization(PSO) was proposed for sparse decomposition of images.The improved PSO uses the fine local search ability of the shrinking chaotic mutation to make the matching pursuit have good global search ability,improves the accuracy and increases the speed of atom matching in the redundant dictionary.The redundant dictionary employs the 2D Mexican hat function as the generating function to represent the edges and contours of images efficiently.Simulation results show that the proposed method is better than the general genetic and PSO algorithms both in speed and visual quality of the reconstructed images.