在红外线与可见光图像的融合过程中, 经常会出现融合图像细节方面缺失的情况。为了解决这一问题, 采用了改进的非下采样轮廓波变换(NSCT)图像融合算法, 融入动态的加权非负矩阵分解规则(WNMF), 对图像进行融合处理。结果表明,利用非下采样轮廓波变换算法对两幅源图像进行多尺度多方向的分解, 可得到低频与高频部分; 动态的WNMF融合规则作为低频部分的融合规则; 高频部分中最高层的分解尺度采用绝对值取大的方法;高频部分其它各层则设定匹配度阈值;低于阈值时, 使用基于区域能量匹配度的区域方差选大的方法; 如果高于阈值时, 采用加权平均的方法进行; 通过对低频部分与高频部分的处理, 用NSCT逆变换方式获得了融合图像。该方法有效提高了融合图像清晰度, 凸显了其细节信息, 缩短了所需的计算时间。

In the process of infrared and visible image fusion, the details of the fusion image are often missing. In order to solve this problem, an improved image fusion algorithm based on nonsubsampled contourlet transform (NSCT) was adopted, which integrated the dynamic weighted non-negative matrix factorization (WNMF) rule into the image fusion. The results show that, the low-frequency and high-frequency parts can be obtained by multi-scale and multi-directional decomposition of two source images by using NSCT algorithm. The dynamic WNMF fusion rule was used as the fusion rule of low frequency parts. The decomposition scale of the highest level in the high frequency part was based on the absolute value method. The matching threshold was set in the other parts of the high frequency part. When the threshold value was lower, the method of region variance based on region energy matching degree was used. When the threshold value is higher, the weighted average method was used. By processing the low frequency part and the high frequency part, the fused image was obtained by NSCT inverse transform. This method effectively improves the definition of the fused image, highlights its details and shortens the calculation time.