基于非下采样Contourlet变换(NSCT)，提出了一种红外和可见光图像融合算法。针对低频子带系数和各带通方向子带系数分别提出了基于图像物理特征的系数加权选择方式与基于区域能量匹配的系数选择方式，即低频基于区域梯度信息、高频基于区域特征因子的加权与选择结合的图像融合算法。实验结果表明：非下采样Contourlet变换具有较快的运算速度，且经非下采样变换后能量更加集中，可提供更多的图像信息。相对于基于像素的图像融合算法，本文的图像融合算法具有更高的融合性能，是一种更适合图像融合的多尺度几何分析(MGA)工具。

A fusion algorithm based on the Nonsubsampled Contourlet Transform(NSCT) is proposed to fuse infrared and visible images. A weighted averaging′ scheme based on the physical features of infrared and visible images and a selection principle based on the local energy matching are presented for the low frequency subband coefficients and the high frequency subband coefficients, respectively. Experimental results show that the NSCT has a fast computing ability and can provide more image information due to the concentration energy by image processing. As compared to the image fusion algorithm based on pixels, this algorithm has higher fusion performance, and is a Multiscale Geometric Analysis(MGA) tool more suitable for image fusion.