A novel algorithm based on Pulse Coupled Neural Network (PCNN) and Wavelet Transform for Multi-focus image fusion was proposed. First

the two original images are decomposed by wavelet transform. Then

based on the PCNN

a fusion rule in the Wavelet domain is given. This algorithm uses the local entropy of wavelet coefficient in each frequency domain as the linking strength

so that its value can be chosen adaptively. After the processing of PCNN with the adaptive linking strength

new fire mapping images are obtained. According to the fire mapping images calculate the firing time gradient maps. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore

by this algorithm

the threshold adjusting constant is estimated by appointed iteration number. Furthermore

In order to sufficient reflect order of the firing time

the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved

each of the wavelet coefficient is activated. Lots of experiments and comparisons with other fusion algorithms show that the method can effectively enhance the edge details and improve the spatial resolution of the image.