结合局部邻域特性和 C-BEMD 的图像融合方法.pdfVIP

第29 卷 第4 期 计算机辅助设计与图形学学报 Vol. 29 No.4 2017 年4 月 Journal of Computer-Aided Design Computer Graphics Apr. 2017 结合局部邻域特性和C-BEMD 的图像融合方法 胡 钢, 郑皎月, 秦新强 (西安理工大学理学院 西安 710048) (hg_xaut@xaut.edu.cn) : 针对基于二维经验模态分解(BEMD) 图像融合方法的不足, 提出一种结合局部邻域特性和可协调二维经验 模态分解(C-BEMD)的图像融合方法. 为了克服BEMD 应用于图像融合时存在的内蕴模函数(IMF)个数和频率不匹配 问题, 通过固定迭代次数和协调操作提出了适合图像融合的C-BEMD 算法; 然后利用C-BEMD 对源图像进行分解获 得IMF 分量和残差分量, 同时对IMF 分量采用基于局部邻域能量的选择与加权平均策略, 而对残差分量则采用基于 局部邻域可见度的融合规则; 最后将融合后的 IMF 分量与残差分量进行叠加, 得到融合后的图像. 融合仿真结果表 明, 该方法对于多聚焦图像、遥感图像和医学图像均可获得视觉效果佳、细节信息丰富的融合图像, 优于基于行列交 叉的经验模态分解和复数经验模态分解的图像融合方法. ：图像融合; 可协调二维经验模态分解; 局部邻域可见度; 局部邻域能量; 选择与加权平均 ：TP391.41 Regional Feature Self-Adaptive Image Fusion Method Based on Coordinated Bidimensional Empirical Mode Decomposition Hu Gang, Zheng Jiaoyue, and Qin Xinqiang (School of Science, Xi ’an University of Technology, Xi’an 710048) Abstract: To conquer the weakness of existing in traditional image fusion method based on bidimensional em- pirical mode decomposition (BEMD), a novel fusion algorithm of multi-sensor images based on coordinated bi- dimensional empirical mode decomposition (C-BEMD) is proposed in this paper. Firstly, the source images are decomposed by C-BEMD to obtain the intrinsic mode function (IMF) components and residue components. Then, for the IMF components, a selection and weighted average fusion rule based on the local area energy is adopted. For the residue components, a selection and weighted average strategy based on local neighborhood visibility is presented. Finally, the fused image is obtained by performing the inverse C-BEMD on the combined coefficients. Experimental results show that the proposed appr