关键词: 多聚焦图像融合, 形态小波, 脉冲耦合神经网络, 改进拉普拉斯能量和, 清晰度

Abstract: A fusion algorithm of multi-focus image fusion is proposed based on morphological wavelet of adaptive Pulse Coupled Neural Networks（PCNN）. Two original images are decomposed by using morphological wavelet separately, thus the low frequency subband coefficients and varieties of directional bandpass subband coefficients are obtained. This algorithm uses the sum-modified-Laplacian of each pixel as the value of the feeding input of each neuron and uses the contrast of each pixel as the value of the linking strength of each neuron. After the processing of PCNN, new fire mapping images are obtained for each coefficient. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. The fused image is obtained by performing the inverse morphological wavelet on the combined coefficients. The experimental results show that the proposed algorithm outperforms traditional fusion algorithms in terms of objective criteria and visual appearance.

Key words: multi-focus image fusion, morphological wavelet, Pulse Coupled Neural Networks（PCNN）, Sum-Modified -Laplacian（SML）, sharpness