摘要
现有融合方法的融合规则不能根据图像的后续使用目的进行自适应调整,不同融合方法的优点也不易综合,为解决这些缺点,提出一种基于粒子群优化算法的融合方法。该方法首先把利用DBSS(2,2)离散小波变换和梯度金字塔融合方法分别产生的图像一起作为初始粒子,然后根据后续处理的要求来构造由多个图像评价指标的加权和所组成的目标函数,再利用粒子群优化算法来优化目标函数从而获取最终的结果图像。两组实验从主观视觉和定量评价指标(标准方差、平均梯度、熵、空间频率、相关系数、均方交叉熵等)两方面证明了该方法的有效性。
To solve the problems that the fusion rule in available image fusion methods can not be adjusted adaptively according to successive application, and the advantages of different fusion methods can not be integrated, a new image fusion method is proposed based on particle swarm optimization. Firstly, the DBSS （2, 2） discrete wavelet transform and gradient pyramid fused images are used to create images as initial particles. Then, according to the requirements of successive application, the object function is constructed by the weighted sum of several image quality assessment criteria. Finally, according to object function, the fused image is obtained by particle swarm optimization. Two sets of images are assessed from visual inspection and quantitative evaluation （standard deviation, average gradient, entropy, spatial frequency, correlation coefficient and mean root cross entropy） to validate the feasibility of proposed method.
出处
《光电工程》
CAS
CSCD
北大核心
2009年第6期109-114,共6页
Opto-Electronic Engineering
基金
国家973重点基础研究发展规划项目(2006CB701304)
关键词
图像融合
评价指标
粒子群优化
小波变换
image fusion
quality assessment
particle swarm optimization
wavelet transform
