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 de-composition 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 compo-nents are eliminated.Compared with the current five mainstream algorithms,the proposed method has the best effect in terms of visual effects and objective indicators.
NETL
NSTL
万方数据
维普
