摘要
针对基于互信息的图像配准方法运行时间长、抗噪声差的问题,提出了一种基于新的相似性测度的图像配准算法,在分析两幅图像的联合直方图点集分布情况的基础上,定义了直方图点集的散度公式,并将其作为相似性测度。为加速参数的搜索过程,配准是在小波域内进行的,并使用遗传算法与Powell算法相结合的方法来优化参数。实验证明,相对于基于互信息的图像配准算法,本算法参数优化方法选择可以更灵活,时间消耗更少,噪声鲁棒性更优。
The image registration algorithm based on mutual information has long runtime and bad antinoise performance. To solve this problem, an image registration algorithm based on a novel similarity measure was proposed. Histogram divergence formula was defined and used as the similarity metric by analyzing the point set distribution of joint histogram. To speed up searching the registration parameters, all were done in the wavelet field and a hybrid algorithm based on genetic algorithm and Powell＇s method was used to optimize this parameters. Experiments show that this algo rithm can apply wider optimization methods and save more time and have better antinoise performance compared with the algorithm based on mutual information.
出处
《激光与红外》
CAS
CSCD
北大核心
2009年第12期1351-1355,共5页
Laser & Infrared
基金
国家自然科学基金项目(No.60572160)资助
关键词
图像配准
直方图散度
小波分解
优化算法
image registration
histogram divergence
wavelet decomposition
optimization algorithm
