基于边缘和多特征选择的多聚焦图像融合

为了有效度量多聚焦图像中的聚焦区域,提出一 种基于边缘和多特征选择的多聚焦图像融合算法。 利用NSCT对源图像进行分解,对得到的低频子带系数采用基于边缘的融合策略；对于高频子 带系数,通过 拉普拉斯能量和、区域能量和方差提取邻域窗口的显著性特征,提出一种新颖的基于多特征 选择的高频融 合策略,该融合策略可以综合考虑区域特征的贡献,自适应的选取显著性特征和融合方法； 最后进行NSCT 逆变换得到融合图像。实验结果表明,与常用的融合方法相比,在主观效果上,本文方法能 够更有效保留 源图像中的边缘和细节信息；在客观指标上,本文方法的融合图像在互信息、边缘保持度、 熵、结构相似度以及标准差等客观评价具有明显的优势。

Multi-focus image fusion based on edge and multi-feature selection

In order to measure effectively the area of focus in a multi-focus im age,a novel multi-focus image fusion algorithm based on edge and multi-feature selection is proposed in this paper.Two registered original images are decomposed using non-subsampled Contarlet transform (NSCT) separately,and the selection principle of the low frequency subband coefficients is based on edges of images.For the high frequency subband coefficients,the prominent features are extracted through Laplace energy sum,region energy and v ariance,and a novel high frequency fusion strategy based on multi-feature selection is proposed.This fu sion strategy can comprehensively consider the contribution of regional features and select promin ent features and fusion methods adaptively.Finally,the fused image is obtained by performing inverse N SCT on the combined coefficients.Compared with common fusion rules,experimental results show that the fusion rule proposed in this paper preserves edges and the details of original images well.Objectively, fusion image of the new fusion rule is more superior in the objective evaluation index,such as mutual i nformation,edge preserving degree,entropy,structural similarity and the standard deviation.