基于限邻域经验模式分解的多波段图像融合

MULTI-BAND IMAGE FUSION ALGORITHM BASED ON NEIGHBORHOOD LIMITED EMPIRICAL MODE DECOMPOSITION

根据不同波段图像信息互补性,提出基于限邻域经验模式分解(NLEMD)的多波段图像融合新算法.将待融合图像进行NLEMD分解,利用其自适应特性及高频细节信息的强获取能力,对不同图像的内蕴模式函数分量和剩余量中的像素按照局部最优原则进行选取,将融合后的内蕴模式函数分量和剩余量反向重构获取融合图像.实验证明该算法具有更强的细节获取能力,融合效果优于传统的基于小波分解的融合算法.

One novel multi-band image fusion algorithm based on neighborhood limited empirical mode decomposition （NLEMD） was proposed. Firstly the images were decomposed by NLEMD, the parts of intrinsic mode functions （IMF＇s） and remnant correspondingly to each image were obtained. Then in these IMF＇s and remnant images, the pixel which has the maximal energy was selected for the result IMF images and remnant image. At last, the final result image was reconstructed. With the help of NLEMD, the image＇s detail can be extracted and the final fusion image is clearer than the source images. Experiments show that the new algorithm has more advantages in achieving high frequency details of source images than the fusion algorithm based on wavelet analysis.