应用光学, 2015, 36 (5): 735, 网络出版: 2015-12-18  

基于非抽样剪切波的加权区域图像融合

Image fusion based on non-sampling shearlets and weighted area feature
作者单位
1 西安建筑科技大学 理学院, 陕西 西安 710055
2 贵州大学 大数据与信息工程学院, 贵州 贵阳 550025
摘要
以提升遥感图像和多聚焦图像的融合精度为目的, 结合非下采样剪切波变换(NSST)可以捕捉图像的细节特征, 提出了一种NSST和加权区域特性的图像融合方法。利用非下采样剪切波变换对源图像进行多尺度、多方向分解, 得到低频子带和高频子带, 低频子带系数采用改进梯度投影的非负矩阵分解(NMF), 高频子带系数采用加权区域能量和区域方差相结合的融合策略, 然后应用非下采样剪切波的逆变换得到融合的图像。实验结果表明: 该方法从主观视觉方面很好地保留了多幅图像的有用信息, 给出该方法与其他融合算法在客观评价指标应用信息熵EN、互信息MI和加权边缘信息量QAB/F的比较结果 。
Abstract
To improve the remote sensing image and multi-focus image fusion accuracy, combined with the non-sampling shearlets transform (NSST) which can capture the details of the image features, we proposed a image fusion method based on NSST and weighted area feature. Firstly this method uses non-sampling shearlets transform for source image to carry on multi-scale multi-direction decomposition to get low-frequency and high-frequency sub-bands.Then the improved gradient projection of non-negative matrix factorization(NMF) is used for the low-frequency sub-band coefficient,while the high frequency sub-band coefficient uses the fusion strategy combining the regional energy and variance of the weighted area. Finally, the non-sampling shearlets inverse transformation is used to get image fusion. The experimental results show that this method can well retain the useful information of multiple images from the aspect of subjective vision,and the comparison results are given with other fusion methods from the aspect of objective evaluation indexes such as entropy, mutual information and weighted edge information preservation values.

李隆, 傅依柳, 柴昱洲, 陈霄鹏, 高当丽, 屈子杰. 基于非抽样剪切波的加权区域图像融合[J]. 应用光学, 2015, 36(5): 735. Li Long, Fu Yiliu, Chai Yuzhou, Chen Xiaopeng, Gao Dangli, Qu Zijie. Image fusion based on non-sampling shearlets and weighted area feature[J]. Journal of Applied Optics, 2015, 36(5): 735.

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