A fusion algorithm for infrared and low-ligh level(visible) images based on neighborhood characteristic and regionalization in NSCT domain was proposed. Firstly, the NSCT was performed on the infrared and visible images at different scales and directions. The low-frequency coefficients were fused with a rule of an improved regional weighted fusion method based on neighborhood energy, and the high-frequency coefficients were fused with a rule of an improved neighborhood energy and regionalization coefficients options and an area variance chooses max based on a neighborhood variance regionalization rule. Finally, the fused coefficients were reconstructed to obtain the fused image. Experimental results show that the information entropy of proposed method is lower than that of the method in the reference whose luminance increases excessively. The introducing of false details of traditional methods results in spatial frequency is slightly higher compared with the proposed method. But the mutual information and edge retention(Q)index value of the proposed method are better and the fusion image is superior to other contrast methods. The proposed method presented better effects in retaining the source images information and capturing details, and fused image had better visual effects.