摘要
提出一种基于限邻域经验模式分解(NLEMD)的多聚焦图像融合算法。该算法利用NLEMD的自适应特性及高频细节信息的较强获取能力,通过对不同图像对应的内蕴模式函数分量IMF中的像素按照局部最大能量,即最优的原则进行选取,然后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。实验结果表明,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。
This paper proposes one new multifocus image fusion algorithm based on Neighborhood Limited Empirical Mode Decomposition （NLEMD）. The images are decomposed by NLEMD, and this algorithm makes full use of adaptive features of NLEMD and the powerful achieving capability of high frequency data to fuse images. In different Intrinsic Mode Functions（lMF） the pixel which has the local max energy is selected as the fusion pixel, and the image is reconstructed using IMFs and remnants. Experiments prove that the new algorithm has more powerful achieving capability of high frequency data and better effect than fusion algorithm based on wavelet analysis.
出处
《计算机工程》
CAS
CSCD
北大核心
2008年第16期230-231,243,共3页
Computer Engineering
基金
国家自然科学基金资助项目"多维信号的局域波分析与应用研究"(60473141)
关键词
限邻域经验模式分解
局域波
图像融合
多聚焦图像
Neighborhood Limited Empirical Mode Decomposition（NLEMD）
local wave
image fusion
multifocus image fusion
