论文标题
改进的双通道脉冲耦合神经网络及其应用于多聚焦图像融合
Improved dual channel pulse coupled neural network and its application to multi-focus image fusion
论文作者
论文摘要
本文介绍了图像融合的改进的双通道脉冲耦合的神经网络（IDC-PCNN）模型。该模型可以克服标准PCNN模型的某些缺陷。在此融合方案中，在双通道PCNN（DC-PCNN）模型的信息融合池中，乘法规则被乘法规则取代。同时，采用了修改的拉普拉斯（SML）度量的总和，这比其他重点措施更好。该方法不仅继承了标准PCNN模型的良好特性，还可以提高计算效率和融合质量。通过使用四个标准，包括平均横熵，均方根误差，峰值信号与噪声比和结构相似性指数，评估所提出的方法的性能。比较研究表明，所提出的融合算法的表现优于标准PCNN方法和DC-PCNN方法。
This paper presents an improved dual channel pulse coupled neural network (IDC-PCNN) model for image fusion. The model can overcome some defects of standard PCNN model. In this fusion scheme, the multiplication rule is replaced by addition rule in the information fusion pool of dual channel PCNN (DC-PCNN) model. Meanwhile the sum of modified Laplacian (SML) measure is adopted, which is better than other focus measures. This method not only inherits the good characteristics of the standard PCNN model but also enhances the computing efficiency and fusion quality. The performance of the proposed method is evaluated by using four criteria including average cross entropy, root mean square error, peak value signal to noise ratio and structure similarity index. Comparative studies show that the proposed fusion algorithm outperforms the standard PCNN method and the DC-PCNN method.
