The fusion of infrared and visible light images can effectively improve the ability of detail description and hot taget representation. For this purpose, a novel image fusion algorithm based on nonsubsampled shearlet transform (NSST) was presented in this paper. Firstly, the NSST was adopted to decompose the two source images at different scales and directions, and the low-frequency and high-frequency sub-band coefficients of the images were obtained. Secondly, we used a modified fusion rules. For the low-frequency coefficients of the fused image, we summed up the low-frequency coefficients of two source images, and then subtracted the average of the mean values of the two low-frequency coefficients. Meanwhile, considering that adjacent pixels had strong correlation, an improved selection principle based on the local energy matching was developed for the high-frequency coefficients of the fused image, which was also consistent with the characteristics of the human vision system. Finally, the fused image was reconstructed by performing the inverse NSST on the combined coefficients. Experimental results demonstrate that the proposed algorithm can effectively integrate important information from infrared and visible light images. And comparing with some other image fusion algorithms, the proposed algorithm can further enhance the contrast of fused images and protect more detail information of source images. Both visual quality and objective evaluation criteria show that the method has a higher performance.