Image Fusion Using Higher Order Singular Value Decomposition

Author

Junli Liang ; Yang He ; Ding Liu ; Xianju Zeng

Author_Institution

Sch. of Autom. & Inf. Eng., Xi´an Univ. of Technol., Xi´an, China

Volume

21

Issue

5

fYear

2012

fDate

5/1/2012 12:00:00 AM

Firstpage

2898

Lastpage

2909

Abstract

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.

Keywords

image fusion; singular value decomposition; tensors; SVD; activity-level measurement; higher order singular value decomposition; image fusion algorithm; informative image patches; local information; sigmoid-function-like coefficient-combining scheme; source images; subtensors; sum of absolute values of the coefficients; Approximation algorithms; Feature extraction; Image fusion; Tensile stress; Transforms; Vectors; Weight measurement; Coefficient-combining strategy; higher order singular value decomposition (HOSVD); image fusion; sigmoid function; Algorithms; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/TIP.2012.2183140

Filename

6126030