Abstract: In this paper, a novel image fusion algorithm based on non-subsampled contourlet transform(NSCT) and pulse coupled neural network(PCNN) was proposed. NSCT provides flexible multiresolution, anisotropy and directional expansion for images. PCNN is a visual cortex-inspired neural network and characterized by the global coupling and pulse synchronization of neurons. For low-frequency sub-images, using sum-modified-Laplacian as the external stimuli of the PCNN, for the high-frequency directional sub-images, the modified spatial frequency are used as the external stimuli of the PCNN. At the same time, the average gradient of each sub-band image is used to adjust the liking strength adaptively. Coefficients with large firing times were selected as the coefficients of the fused image. Experimental results show that the proposed scheme can significantly improve image fusion performance and outperforms the conventional algorithms such as wavelet transform, contourlet transform and PCNN in term of objective criteria and visual appearance.