一种基于PCNN的多聚焦图像融合改进算法

为提高图像融合质量和融合效率，提出一种基于脉冲耦合神经网络的多聚焦图像融合改进算法。对待融合的源图像作分块处理，选取合理的图像质量评价指标，计算每个分块的指标值，归一化后相减得到指标差值。把指标差值作为外部刺激输入到PCNN模型中，得到脉冲输出结果。用脉冲输出与给定的阈值作比较，若输出脉冲超过阈值则选择指标值大的源图像块作为融合图像块，否则取指标值小的源图像块。选取互信息、交叉熵、均方根误差、峰值信噪比、结构相似度以及相关系数6个客观质量评价指标进行评价，实验结果表明，该算法可获得较好的图像融合效果。

An Improved Multi-focus Image Fusion Algorithm Based on PCNN

An improved multi-focus image fusion algorithm based on Pulse Coupled Neural Network(PCNN) is proposed for improving the fusion quality and the fusion efficiency. The image quality evaluation index of each block is calculated, which is selected with enough reason. The difference of the index is obtained by subtracting the two normalized indices. The difference of the index is input into PCNN model as external stimulus, and the output pulse is obtained. Comparing the value of output pulse with a given threshold, the fused image block is selected from the source image block whose evaluation index is large, while pulse output is larger than threshold. Take the source image block with small value of evaluation index. The performance of the proposed method is evaluated using six criteria including mutual information, cross entropy, root mean squared error, peak value signal-to-noise ratio, structure similarity index and correlation coefficient. Experimental result shows that the algorithm can improve image fusion effect.