An application of swarm intelligence binary particle swarm optimization (BPSO) algorithm to multi-focus image fusion
作者： Gang Chen , Jiuqiang Han , Lubing Sun , Xinman Zhang
DOI:
关键词: Image fusion 、 Multi-swarm optimization 、 Contourlet 、 Swarm intelligence 、 Wavelet 、 Artificial neural network 、 Salience (neuroscience) 、 Algorithm 、 Segmentation 、 Computer science
摘要: In this paper, an optimal and intelligent multi-focus image fusion algorithm is presented, expected to achieve perfect reconstruction or of images with high speed. A synergistic combination segmentation techniques binary particle swarm optimization (BPSO) search strategies employed in salience analysis contrast feature-vision system. Also, several evaluations concerning definition are exploited used evaluate the performance method proposed. Experiments performed on a large number results show that BPSO much faster than traditional genetic algorithm. The proposed also compared some classical new methods, such as discrete wavelet-based transform (DWT), nonsubsampled contourlet (NSCT), NSCT-PCNN (pulse coupled neural networks (PCNN) NSCT domain) curvelet transform. simulation accuracy speed prove superiority effectiveness present method.
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