结合潜在低秩分解和稀疏表示的脑部图像融合

针对低秩分解和稀疏表示(space representation,SR) 造成融合图像信息缺失的问题,提出一种结合潜在低秩分解和SR的脑部图像融合算法。首先,将源图像分解为低秩、稀疏和噪声3种成分,面对不同分解成分特性间的差异,分别构造低秩字典和稀疏字典进行描述:采用加权灰度值的方法处理低秩成分,以保持其轮廓和亮度特征；对于稀疏成分,设计一种多范数加权度量的方法对SR进行改进,以保持其高维信息,剔除噪声成分。比对当前主流的5种算法,在视觉效果和客观指标上,本文方法效果最优。

Brain image fusion combining latent low-rank decomposition and sparse representation

In order to solve the problem that the fusion algorithm of low-rank decomposition and sparse representation (SR) causes a lot of information missing,a brain image fusion algorithm combining latent low-rank decomposition and SR is proposed.Firstly,the source image is decomposed into low-rank,sparse and noisy components.In the face of the differences between the characteristics of different decomposition components,the low-rank and sparse dictionaries are constructed to describe the low-rank components respectively.The weighted gray value method is used to process low-rank components to maintain their contour and brightness features. For the sparse components,a multi-norm weighted metric method is designed to improve the SR to maintain the high-dimensional information.The noise components are eliminated.Compared with the current five mainstream algorithms,the proposed method has the best effect in terms of visual effects and objective indicators.