A novel image fusion algorithm based on the Stationary Lifting Wavelet Transform (SLWT) and adaptive Pulse Coupled Neural Network (PCNN) is proposed. Compared with the traditional PCNN where the linking strength of each neuron is the same, this adaptive PCNN uses the sharpness of each pixel as its value, so that the linking strength of each pixel can be chosen adaptively. By using a stationary lifting wavelet transform, we can calculate a flexible multiscale and shift-invariant representation of registered images. A Novel Sum-Modified-Laplacian (NSML) in the low frequency subbands and the pixels value of high frequency subbands of SLWT are input into motivate adaptive PCNN, respectively. The coefficients in SWLT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform-based SLWT-based and SLWT-PCNN-based image fusion methods in terms of both visual quality and objective evaluation.