Quantum-inspired particle swarm optimization algorithm with performance evaluation of fused images

In order to improve and accelerate the speed of image integration, an optimal and intelligent method for multi-focus image fusion is presented in this paper. Based on particle swarm optimization and quantum theory, quantum particle swarm optimization (QPSO) intelligent search strategy is introduced in salience analysis of a contrast visual masking system, combined with the segmentation technique. The superiority of QPSO is quantum parallelism. It has stronger search ability and quicker convergence speed. When compared with other classical or novel fusion methods, several metrics for image definition are exploited to evaluate the performance of all the adopted methods objectively. Experiments are performed on both artificial multi-focus images and digital camera multi-focus images. The results show that QPSO algorithm is more efficient than non-subsampled contourlet transform, genetic algorithm, binary particle swarm optimization, etc. The simulation results demonstrate that QPSO is a satisfying image fusion method with high accuracy and high speed.