基于非下采样Shearlet变换耦合导向法则的多聚焦图像融合算法  

Multi-focus image fusion algorithm based on non-subsampled shearlet transform and guidance rule

作　　者：杨建翠 马庆功[2] Yang Jiancui;Ma Qinggong(Jiangsu Vocational College of Medicine,Yancheng 224005,China;Changzhou University,Changzhou 213016,China)

机构地区：[1]江苏医药职业学院,盐城224005 [2]常州大学,常州213016

出　　处：《电子测量与仪器学报》2020年第3期36-42,共7页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61272367);江苏省高校自然科学基金面上项目(16KJB520001)资助

摘　　要：为了克服当前较多图像融合算法主要是通过取大法来完成图像系数的融合,忽略了图像间的关联性,导致融合图像中含有间断及振铃现象等缺陷,设计了基于非下采样Shearlet变换耦合导向法则的多聚焦图像融合算法。首先,引入非下采样Shearlet变换(NSST),对多聚焦图像进行计算,求取图像的不同系数。再利用图像的区域能量、标准差以及空间频率特征,对图像的关联性进行度量,并将度量结果作为选择融合规则的导向信息,通过构造导向法则来完成低频系数融合。在高频系数融合时,利用图像的均值特征以及Laplacian能量特征,分别对图像的亮度以及边缘信息进行度量,以实现高频系数的融合。以电路板与仪表盘为样本数据进行测试,结果显示,与当下融合算法相比,本文算法具有更高的融合效果,其输出图像具有更大的通用图像质量指标与平均梯度值。In order to overcome the shortcomings of many current image fusion algorithm, such as discontinuity and ringing, which are mainly achieved by taking large image coefficients and ignoring the correlation between images, a multi-focus image fusion algorithm based on non-subsampled shearlet transform and guidance rule is designed in this paper. Firstly, the non-down sampling Shearlet transform(NSST) is introduced to calculate the multi-focus image and obtain the different coefficients of the image. Secondly, the image correlation is measured by using the regional energy, standard deviation and spatial frequency characteristics of the image, and the measurement results are used as guidance information for selecting fusion rules, and the low-frequency coefficient fusion is completed by constructing guidance rules. When high frequency coefficients are fused, the brightness and edge information of the image are measured by means of the mean value feature of the image and the Laplacian energy feature, respectively, in order to achieve the fusion of high frequency coefficients. The experimental results show that, compared with the current fusion algorithm, the fusion image quality of this algorithm is better and has better fusion performance.

关 键 词：NSST 导向法则 Laplacian能量特征 标准差特征 导向信息 图像融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN409[自动化与计算机技术—计算机科学与技术]