摘要
针对Curvelet分解的不同频率域,分别讨论了低频系数和高频系数的选择原则。在选择低频系数时,定义了局部区域标准方差,采用了“选择”与“平均”相结合的系数选择方案;在选择高频系数时,充分利用Curvelet变换具有方向性的优点,提出了Curvelet域方向对比度的概念,并给出了基于方向对比度的系数选择方案。实验结果表明:本文所给出的融合算法能够很好地保留多幅源图像中的有用信息,得到多个目标聚焦都很清晰的图像。
According to the different frequency areas decomposed by Curvelet transform, the selection principles of the low frequency coefficients and the high frequency coefficients were discussed respectively. In choosing the low frequency coefficient, the concept of the local area standard deviation was defined and a scheme combining the “selection” and the “average” methods was employed. In choosing the high frequency coefficient, taking the advantage of the directional sensitivity characteristic of the Curvelet transform, the concept of directional contrast in the Curvelet domain was defined and a selection principle based on the direction contrast was presented. The experimental results show that the proposed algorithm can extract all useful information from the original images and make all targets in the fused images very clear.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2007年第2期458-463,共6页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金资助项目(60572152)
'863'国家高技术研究发展计划项目(2006AA01Z127)
教育部优秀青年教师资助计划项目
