Compressive Image Fusion Based on Particle Swarm Optimization Algorithm

In this paper, we propose a novel compressive image fusion method based on multi-objective particle swarm optimization. In compressive image fusion, the challenge is to choose proper fusion parameter, particle swarm optimization who is a based stochastic optimization technique can solve the challenge. In order to get appropriate parameter, the fitness function select the average gradient function, data similarity function, standard deviation function. The experimental results indicate that the proposed method in MI, Qw, Qe, QAB vertical bar F four evaluation indexes have better performance, our method can get more information from the source images and retained more structural information and edge information.