摘要
针对传统基于多尺度变换的图像融合方法存在的缺点,提出了一种基于平移不变剪切波变换域的自适应图像融合新方法.首先,使用平移不变剪切波变换对源图像进行分解,得到低频子带及方向带通子带系数.然后,对于低频子带系数采用梯度域奇异值分解方法估计图像的局部结构信息,提出了基于提取的特征与S函数的可变加权融合策略;对于各方向带通子带系数,提出了一种基于改进的拉普拉斯能量和匹配的"加权平均"和选择相结合的系数选择策略.最后,对得到的融合系数进行逆变换得到融合图像.通过实验可以发现相比于传统的图像融合方法,本文方法得到了更高的客观指标,融合图像视觉效果更好.
To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on shift-invariant shearlet transform （SIST） is proposed. Firstly, the SIST is utilized to decompose the source images, and the low frequency sub-band coefficients and directional bandpass sub-band coefficients are obtained. Secondly, for the low frequency in the gradient domain is used to sub-band coefficients, the singular value decomposition method estimate the local structure information of image, and a variable weights fusion scheme based on the sigmoid function and the extracted features is presented, while for the directional bandpass sub-band coefficients, a scheme based on the Sum-modified- 1.aplacian （SML） combined with the weighted average scheme is presented. Finally, the fused image is obtained by performing the inverse SIST on the combined coefficients. The experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2013年第4期496-503,共8页
Acta Photonica Sinica
基金
安徽省自然科学基金(No.1308085MA09)
安徽省教育厅重点科研项目(No.KJ2010A282)
合肥工业大学博士专项科研基金(No.2012HGBZ0653)
国家级大学生创新创业训练计划项目(No.201210359062)资助
关键词
图像融合
平移不变剪切波变换
奇异值分解
S函数
Image fusion
Shift-invariant shearlet transform
Singular value decomposition
Sigmoid function
