结合潜在低秩分解和稀疏表示的脑部图像融合
Brain image fusion combining latent low-rank decomposition and sparse representation
张亚加 1邱啟蒙 2刘恒 2邵建龙2
作者信息
- 1. 昆明理工大学信息工程与自动化学院,云南昆明 650500;云南开放大学城市建设学院,云南昆明 650500
- 2. 昆明理工大学信息工程与自动化学院,云南昆明 650500
- 折叠
摘要
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.
Abstract
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.
关键词
潜在低秩分解/多范数加权度量/脑部图像/稀疏表示(SR)/融合指标Key words
latent low-rank decomposition/multiple-norm weighted metric/brain images/sparse repre-sentation(SR)/fusion indicators引用本文复制引用
基金项目
国家自然科学基金(61302042)
昆明理工大学教育技术研究项目(2506100219)
出版年
2023
NETL
NSTL
维普
万方数据