For enhancing fusion accuracy of infrared and visible images, an adaptive fusion algorithm of infrared and visible images based on Local Nonsubsampled Shearlet Transform (LNSST) and Pulse Coupled Neural Networks (PCNN)is proposed. First,source images are decomposed to multi-scale and multi-direction subband images by LNSST. Secondly,blocked singular value decomposition of each subband image is done to calculate the area feature energy value which is served as linking strength of each neuron in PCNN. After the processing of PCNN with the adaptive linking strength, new fire mapping images of the entire subband images are obtained, the clear objects of subband images are selected by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a group of new clear subband images. Finally, fused subband images are reconstructed to image by local nonsubsampled shearlet inverse transform. Some fusion experiments on several sets of infrared and visible images are done and objective performance assessments are implemented to fusion results. The experimental results indicate that the proposed method performs better in subjective and objective assessments than a few existing typical fusion techniques in the literatures and obtains better fusion performance.