为改进红外与微光 /可见光的图像融合效果, 在对二维经验模式分解及图像区域分割研究的基础上, 提出了一种基于区域的二维经验模式分解的图像融合算法。利用此算法对红外图像与微光 /可见光图像进行融合, 先将源图像分别进行二维经验模式分解, 再对残余图像进行加权融合, 而后用模糊 C均值聚类的方法对融合后的残余图像进行区域分割, 将此分割结果映射到各层本征模式函数图像上, 随后运用一定的区域融合准则将各层图像融合, 最后再重构出融合图像。对仿真实验结果使用客观评价的方法进行评价, 评价结果表明, 该算法能够提升融合图像中的信息量以及凸显图像细节, 较其它传统算法具有一定的优势。

In order to improve the effect of the image fusion, a region-based image fusion algorithm using bidimensional empirical mode decomposition(BEMD)was put forward. This algorithm can be used to fuse the infrared image and low-level-light or visible image. First of all, decompose the source images by BEMD and fuse the residue of the images by weighted average. Secondly, segment the fused image by fuzzy C-means(FCM)and use the result to map the intrinsic mode function(IMF)images. Then fuse the IMF images by some fusion criterion. Finally, reconstruct the fusion images. The simulation results and objective evaluation data show that such algorithm can enhance the information in the fused image and highlight the image details. And this algorithm has certain advantages compared with others.