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.