针对多聚焦图像融合中目标物边缘处产生虚影的问题,提出一种基于引导滤波与改进脉冲耦合神经网络(PCNN)的多聚焦图像融合算法。该算法利用引导滤波器对源图像进行多尺度边缘保持分解,对分解得到的基本图像和细节图像采用不同的引导滤波加权融合策略进行初步融合;将初步融合图作为外部输入激励刺激改进的PCNN模型;根据融合权重图对多幅源图像进行融合,获得最终的融合图像。实验结果表明,与传统融合算法相比,本文方法较好地保留了源图像的边缘、区域边界以及纹理等细节信息,避免了目标物边缘处产生虚影,提高了融合图像的质量。

To solve the problem that multi-focus image fusion results in virtual shadow at the target object edge, a multi-focus image fusion algorithm is proposed based on the guided filtering and improved pulse coupled neural network (PCNN). The source image is decomposed by a guided filter with the multi-scale edge-preserving decomposition, and the preliminary fusion, and the obtained basic and detail images are fused preliminarily by different guided filtering weighted fusion strategies. Preliminary fusion image is used as external input excitation to stimulate the improved PCNN model. The source images are according to the fusion weight map to obtain the final fusion image. Experimental results show that, compared with traditional fusion algorithms, the detail information of edge, region boundary and texture of source images are preserved by the proposed algorithm, which avoids virtual shadow at target object edge, and improves fusion image quality.