基于NSUDCT的红外与可见光图像融合

针对同一场景的红外与可见光图像，提出了基于非下采样均匀离散Curvelet 变换（NSUDCT）的图像融合方法。首先使用标记控制的分水岭分割（MCWS）算法对源图像进行区域分割，对各分割结果进行叠加得到联合区域图。然后对源图像进行非下采样均匀离散Curvelet 分解，分解后的低频系数采用区域对比度和区域标准差作为量测指标进行融合，高频方向系数使用基于局部能量的融合规则进行融合，并对融合系数做一致性检测。最后通过各频带融合系数重建得到融合图像。实验结果表明文中方法取得了比较好的视觉效果和量化数据，相比基于NSUDCT 的像素融合方法，此文方法的熵值提高了9.87%，交叉熵减少了68.04%,互信息提高了80%。

Fusion of infrared and visible images based on NSUDCT

Aiming at the infrared and visible images in a same scene, a novel fusion algorithm based on the nonsubsampled uniform discrete curvelet transform (NSUDCT) was proposed. First, the source images were segmented using the marker controlled watershed segmentation (MCWS), and the joint region graph was obtained by superimposing the segmented results. Then, the nonsubsampled uniform discrete Curvelet transform was applied to the source images, the low-frequency coefficients were fused with the measurement of ratio of region contrast and region standard deviation, the high-frequency directional coefficients were fused with the local energy fusion rule, and the consistency of the fused coefficients was examined. Finally, the fused image was reconstructed from the subband fused coefficients. The experiment results indicate that the proposed method could provide better fusion quality in terms of both visual and quantified measure. Compared with the pixel fusion method based on NSUDCT, the Entropy of fused images increased by 9.87% , the Cross Entropy decreased by 68.04% and the Mutual Information increased by 80%.