A Novel Image Fusion Method Based on Particle Swarm Optimization

摘要

In most of multi object image fusion methods, the parameter configuration of fusion model is usually based on experience. In this paper, a new multi objective optimization method of multi objective image fusion based on APSO (Adaptive Particle Optimization) is presented, which can simplify the model of multi objective image fusion and overcome the limitations of traditional methods. First the proper evaluation indices of multi objective image fusion are given, then the uniform model of multi objective image fusion in DWT (Discrete Wavelet Transform) domain is constructed, in which the model parameters are selected as, the decision variables, and finally APSO is designed to optimize the decision variables. APSO not only uses a mutation operator to avoid earlier convergence, but also uses a crowding operator to improve the distribution of no dominated solutions along the Pareto front, and uses a new adaptive inertia weight to raise the optimization capacities. Experiment results demonstrate that APSO has a higher convergence speed and better search capacities, and that the method of multi objective image fusion based on IMOP- SO achieves the Pareto optimal image fusion.