A fusion method of infrared and visible light images based on Pulse Coupled Neural Network (PCNN) and wavelet transform is studied.Firstly, the two original images are decomposed by wavelet transform, then, a fusion rule in the wavelet domain is given based on the PCNN.This algorithm uses the local entropy of wavelet coefficient in each frequency domain as the linking strength, then its value can be chosen adaptively.After processing PCNN with the adaptive linking strength, new fire mapping images are obtained.According to the fire mapping images, the firing time gradient maps are calculated and the fusion coefficients are decided by the compare-selection operator with firing time gradient maps.Finally, the fusion images are reconstructed by wavelet inverse transform.Two groups of experiments are undertaken for the fusion of visible and infrared images,results indicate that when the numbers of iterations are 50 times, the entropy has increased by 1.1% and 0.7%; the average grads by 8.3% and 3.7%; the spatial frequencies by 2.5% and 1.5%; the standard deviation by 1.9% and 0.6%, respectively; and the cross-entropy has reduced by 5.6% and 4.9%, respectively as comparing with that of classical wavelet method.These results show that proposed method has improved the details of fused images and is suitable for fusing visible and infrared images.