针对SAR图像与可见光图像融合的特点, 提出一种基于非下采样Contourlet变换(NSCT)的SAR图像与可见光图像融合算法。通过NSCT变换对SAR图像和可见光源图像进行分解, 对低频子图像, 采用以区域熵为测度参数进行邻域融合。对高频子图像的最高层采用区域标准差选大法进行融合; 对高频子图像的其他层采用以邻域相关系数为阈值, 基于平均梯度选择的邻域算法进行融合。最后进行NSCT逆变换得到最终融合图像。实验结果表明该方法可以获得较理想的融合图像和更多的细节信息。

Aiming at the fusing characteristics of Synthetic Aperture Radar (SAR) images and optical images, an algorithm for image fusion based on Non-subsampled Contourlet Transformation (NSCT) is proposed. The SAR images and optical source images are decomposed by NSCT transformation. To the low-frequency sub-images, the local entropy is taken as the measure parameter to carry out neighborhood fusion. To the top layer of the high-frequency sub-images, the fusion method of “choosing the biggest” for the regional standard deviation is adopted. As to the other layers of the high-frequency sub-images, the neighborhood correlation coefficient is taken as the threshold, and the neighborhood algorithm based on average gradient selection is adopted to carry out the fusion. Finally, NSCT inverse transformation is utilized to obtain the final fusion image. The experimental results show that the ideal fusion image and more detailed information can be obtained by using this method.