Abstract: The traditional fusion methods based on Pulse Coupled Neural Network (PCNN) have the shortcomings of too many parameters, the parameters and number of network iterations difficult to accurately set, and poor fusion effect. In order to solve the problems, a new image fusion algorithm using the connection item (L item) of Linking Synaptic Computing Network (LSCN) model was proposed. Firstly, the two images to be fused was input into the LSCN model respectively. Secondly, the L term was used to replace the ignition frequency in the traditional PCNN as the output. Then, the iteration was terminated by the multi-pass operation. Finally, the pixels of the fused image were obtained by comparing the values of L terms. The theoretical analysis and experimental results show that, compared with the image fusion algorithms using the improved PCNN model and the new model proposed on the basis of PCNN model, the fusion images generated by the proposed algorithm have better visual effects. In addition, compared with the fusion algorithm of LSCN using ignition frequence as the output, the proposed algorithm is all superior in edge information evaluation factor, information entropy, standard deviation, space frequency, average grads. The proposed algorithm is simple and convenient, which not only reduces the number of parameters to be determined, reduces the computational complexity, but also solves the problem that the number of iterations in the traditional model is difficult to be determined.

Key words: Linking Synaptic Computation Network (LSCN), ignition frequency, image fusion, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Pulse Coupled Neural Network (PCNN)

摘要： 针对传统的脉冲耦合神经网络（PCNN）融合方法中参数过多，以及参数和网络迭代次数难以准确设置、融合效果差等缺点，提出了一种用连接突触计算网络（LSCN）模型的连接项（L项）进行图像融合的算法。首先，把两幅待融合图像分别输入到LSCN模型中；其次，使用L项代替传统PCNN中的点火频率作为输出；然后，使用多通工作方式终止迭代；最后，通过比较L项的值得到融合后图像的像素。理论分析与实验结果表明，与改进的PCNN模型和在PCNN模型的基础上提出的新模型进行图像融合的算法进行比较，所提算法得到的融合图像更有利于人眼观察；特别是与点火频率作为输出的LSCN方法相比，所提算法在边缘信息评价因子、信息熵、标准差、空间频率、平均梯度上均较优。该算法简单易行，不仅减少了待定参数数目，降低了计算复杂度，而且解决了传统模型中迭代次数难以确定的问题。

关键词: 连接突触计算网络, 点火频率, 图像融合, 计算机断层扫描, 磁共振成像, 脉冲耦合神经网络