In order to represent a scene exactly and entirely

an image fusion method based on Pulse Coupled Neural Network (PCNN) is proposed. After registering the images of multi-source sensors

obtained images are decomposed into several coefficients of low frequency and high frequency by using the 9/7 wavelet transform based on lifting scheme. The larger absolute gray values are selected to fuse low frequency images and the high frequency images are input to the PCNN

then a serial of fused sub-images can be obtained by comparing firing times after the iteration. Finally

the fused images are obtained by inversing transform using the 9/7 wavelet based on lifting scheme. By means of design of simulation experiments using visible and infrared images

the entropy

average gradient

standard deviation and space frequency are selected to evaluate the fused image. Obtained results show that the entropy

average gradient

standard deviation and space frequency of the fused image by using the novel fusion method base on PCNN are higher 0.010 4

0.245 9

0.113 1 and 0.284 6

respectively

than those by using the fusion method combining traditional wavelet and PCNN when a standard source image is used.