Image Fusion Using Higher Order Singular Value Decomposition

被引：102

作者：

Liang, Junli ^{[1
]}

He, Yang ^{[1
]}

Liu, Ding ^{[1
]}

Zeng, Xianju ^{[2
]}

机构：

[1] Xian Univ Technol, Sch Automat & Informat Engn, Xian 710000, Peoples R China

[2] Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2012年 / 21卷 / 05期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Coefficient-combining strategy; higher order singular value decomposition (HOSVD); image fusion; sigmoid function;

D O I：

10.1109/TIP.2012.2183140

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel higher order singular value decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.

引用

页码：2898 / 2909

页数：12

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