Segmentation-Driven Image Fusion Based on Alpha-Stable Modeling of Wavelet Coefficients
Segmentation-Driven Image Fusion Based on Alpha-Stable Modeling of Wavelet Coefficients
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DOI：
10.1109/tmm.2009.2017640
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发表时间：
2009-06-01
影响因子：
7.3
通讯作者：
Achim, Alin
中科院分区：
文献类型：
--
作者：
Wan, Tao;Canagarajah, Nishan;Achim, Alin
提出了一种基于多尺度图像分割和统计特征提取的区域图像融合框架。采用双树复小波变换（DT-CWT）和统计区域合并算法生成源图像的区域图。输入图像通过对称α稳定（S α S）分布被划分为包含显著信息的有意义的区域。然后使用双变量α稳定（B α S）分布对区域特征进行建模，并且将源图像的对应区域之间的相似性的统计测量计算为估计的B α S模型之间的Kullback-Leibler距离（KLD）。最后，一个分割驱动的方法是用来融合的图像，逐区域，在复小波域。通过考虑区域内的局部统计特性，引入了一种新的决策方法，这显着提高了特征选择和融合过程的可靠性。仿真结果表明，双变量α稳定模型不仅能捕捉到子带边缘分布的重尾特性，而且能捕捉到不同尺度下小波系数之间的强统计相关性，优于单变量α稳定模型和广义高斯密度模型.实验表明，我们的算法取得了更好的性能相比，以前提出的像素级和区域级融合方法在主观和客观评价测试。
A novel region-based image fusion framework based on multiscale image segmentation and statistical feature extraction is proposed. A dual-tree complex wavelet transform (DT-CWT) and a statistical region merging algorithm are used to produce a region map of the source images. The input images are partitioned into meaningful regions containing salient information via symmetric alpha-stable (S alpha S) distributions. The region features are then modeled using bivariate alpha-stable (B alpha S) distributions, and the statistical measure of similarity between corresponding regions of the source images is calculated as the Kullback-Leibler distance (KLD) between the estimated B a S models. Finally, a segmentation-driven approach is used to fuse the images, region by region, in the complex wavelet domain. A novel decision method is introduced by considering the local statistical properties within the regions, which significantly improves the reliability of the feature selection and fusion processes. Simulation results demonstrate that the bivariate alpha-stable model outperforms the univariate alpha-stable and generalized Gaussian densities by not only capturing the heavy-tailed behavior of the subband marginal distribution, but also the strong statistical dependencies between wavelet coefficients at different scales. The experiments show that our algorithm achieves better performance in comparison with previously proposed pixel and region-level fusion approaches in both subjective and objective evaluation tests.