结合潜在低秩分解和稀疏表示的脑部图像融合
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(1.昆明理工大学 信息工程与自动化学院,云南 昆明 650500; 2.云南开放大学 城市建设学院,云南 昆明 650500)

作者简介:

邵建龙 (1965-),男,教授,研究方向为智能信息处理与虚拟现实.

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国家自然科学基金(61302042)和昆明理工大学教育技术研究项目(2506100219)资助项目


Brain image fusion combining latent low-rank decomposition and sparse representation
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(1.School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China;2.College of Urban Construction, Yunan Open University, Kunming, Yunnan 650500, China )

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    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 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.

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张亚加,邱啟蒙,刘恒,邵建龙.结合潜在低秩分解和稀疏表示的脑部图像融合[J].光电子激光,2023,34(11):1225~1232

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  • 收稿日期:2022-05-21
  • 最后修改日期:2022-10-17
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  • 在线发布日期: 2023-11-16
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