A new effective image fusion algorithm based on NSCT and PCNN

被引：1

作者：

Shen, Chao ^{[1
,2
]}

Gao, Wei ^{[1
]}

Song, Zongxi ^{[1
]}

Yao, Tong ^{[1
,2
]}

Li, Bin ^{[1
,2
]}

Li, Feipeng ^{[1
,2
]}

Wu, Mengjie ^{[1
,2
]}

机构：

[1] Space Optics Laboratory, Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an

[2] University of Chinese Academy of Sciences, Beijing

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 10期

关键词：

Contourlet; Image fusion; NSCT; PCNN; SF;

D O I：

10.12733/jics20106168

中图分类号：

学科分类号：

摘要：

NonSubsampled Contourlet Transform (NSCT) has the characteristics of multi-scale, multi-directional, multi-resolution and shift invariance. Because of down sampling, traditional contourlet transform will cause Gibbs phenomenon, NSCT can overcome the disadvantage, obtaining better fusion image. Due to Pulse Coupled Neural Networks (PCNN) excellent biological characteristics, it has already been widely applied to image processing. In this paper, we will combining NSCT and PCNN, making full use of their advantages and applying to image fusion. The original image is by NSCT transform, getting the decomposition coefficients and then calculating its spatial frequency, input them to PCNN. According to the firing times select fusion coefficients. Experiment result has shown that, compared with the typical wavelet transform and contourlet transform method, the method proposed in this paper whether in subjective or objective is better than other methods. 1548-7741/Copyright © 2015 Binary Information Press

引用

页码：4137 / 4144

页数：7

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