An application of swarm intelligence binary particle swarm optimization (BPSO) algorithm to multi-focus image fusion

In this paper, an optimal and intelligent multi-focus image fusion algorithm is presented, expected to achieve perfect reconstruction or optimal fusion of multi-focus images with high speed. A synergistic combination of segmentation techniques and binary particle swarm optimization (BPSO) intelligent search strategies is employed in salience analysis of contrast feature-vision system. Also, several evaluations concerning image definition are exploited and used to evaluate the performance of the method proposed. Experiments are performed on a large number of images and the results show that the BPSO algorithm is much faster than the traditional genetic algorithm. The method proposed is also compared with some classical or new fusion methods, such as discrete wavelet-based transform (DWT), nonsubsampled contourlet transform (NSCT), NSCT-PCNN (pulse coupled neural networks (PCNN) method in NSCT domain) and curvelet transform. The simulation results with high accuracy and high speed prove the superiority and effectiveness of the present method.

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