Novel image fusion algorithm based on local contrast and adaptive PCNN

被引：0

作者：

Miao, Qi-Guang ^{[1
]}

Wang, Bao-Shu ^{[1
]}

机构：

[1] School of Computer Science, Xidian University

来源：

Jisuanji Xuebao/Chinese Journal of Computers | 2008年 / 31卷 / 05期

关键词：

Fire mapping image; Human vision system; Image fusion; Linking strength; Local contrast; Pulse-Coupled Neural Network (PCNN);

D O I：

10.3724/sp.j.1016.2008.00875

中图分类号：

学科分类号：

摘要：

This paper proposes a new fusion algorithm based on the improved pulse coupled neural network(PCNN) model, the fundamental characteristics of images and the properties of human vision system. Compared with the traditional algorithm where the linking strength of each neuron has the same value and its value is chosen through experimentation, this algorithm uses the local contrast of each pixel as its value, so that the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image taking part in the fusion. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. Furthermore, by this algorithm, other parameters, for example, Δ, the threshold adjusting constant, only have a slight effect on the new fused image. It therefore overcomes the difficulty in adjusting parameters in PCNN. Experimental results indicate that the method outperforms the traditional approaches in preserving edge information while improving texture information.

引用

页码：875 / 880

页数：5

共 19 条

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