针对源图像有用信息的提取，提出了基于区域分维和非下采样Contourlet变换相结合的红外与可见光图像融合算法.将图像的区域属性、区域大小、边缘强度以及纹理显著程度等特点用图像不同尺度上的区域分维进行描述，对于非下采样Contourlet变换低频系数，根据源图像不同尺度上的区域分维进行基于系数选择的融合.针对带通子带系数设计了系数局部匹配度算子，依据匹配度不同采用加权和系数选取相结合的融合规则.与其他常规融合方法进行比较，该算法可有效实现红外与可见光图像的融合.

As an important problem in the course of image fusion,the extraction of useful information from source images affects the fusion result directly,and it is all along one of the research focuses and difficult ponits of image fusion.A novel image fusion method for infrared and visible images based on nonsubsampled contourlet transform and fractal dimension is proposed.The regional properties,size of regions,intensity of edges and the strength of texture are all described by the regional fractal dimensions in different scales.Through selecting coefficients from two source images based on regional fractal dimension,the low frequency subband coefficients of the source images are fused.The bandpass directional coefficients are fused using different strategies based on the matching operator proposed.Compared with other methods,experimental results show that the proposed algorithm is effective and feasible.