局部梯度极值点的BEMD与CI算法的图像融合增强

Image Fusion Enhancement Based on CI Algorithm and Local Gradient Extrema Based BEMD

协方差交叉算法是分布式信息融合中通过优化一定目标函数得到的一种分布式融合估计方法,这种方法为图像融合增强提供了一种新思路。提出基于局部梯度极值点的二维经验模式分解(BEMD)与协方差交叉(CI)算法的图像融合方法,针对传统BEMD获取图像细节能力的不足,为使图像包含更多细节结构特征,根据梯度对图像细节信息的强挖掘能力,采用4个二维方向上的极值条件选取局部梯度极值点对图像进行经验模式分解并确定内蕴模式函数。将一维协方差交叉算法扩展到二维信号和图像融合上,通过最小化各内蕴模式函数的二维协方差交叉阵的"模"范数计算最优线性加权阵,并利用反向重构得到融合增强图像。经仿真实验分析发现,与传统的图像融合算法对比,所提方法具有更强的细节捕捉能力,清晰度明显提升。

The covariance intersection algorithm is a distributed fusion estimation method obtained by optimizing certain objective functions which provides a new idea for image fusion enhancement.An image fusion method based on the Covariance Intersection(CI)algorithm and the local gradient extrema based Bidimensional Empirical Mode Decomposition(BEMD)is proposed.To overcome the inadequacy of traditional BEMD in obtaining image details and to include more detailed structure features in the image according to the strong ability of the gradient to mine the detailed information of the image local gradient extrema are selected by using four two-dimensional extremum conditions.Then empirical mode decomposition of the image is carried out and the IMF is determined.Then the one-dimensional covariance intersection algorithm is extended to 2D signals and image fusion.The optimal linear weighting matrix is computed by minimizing the F-norm of the 2D covariance intersection matrix of each IMF.The enhanced fusion image is obtained by using inverse reconstruction.The simulation results show that compared with the traditional image fusion algorithm the proposed method has stronger detail capture ability and better clarity.