Multi-focus image fusion using PCNN

被引：232

作者：

Wang, Zhaobin ^{[1
,2
]}

Ma, Yide ^{[1
]}

Gu, Jason ^{[2
]}

机构：

[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China

[2] Dalhousie Univ, Dept Elect & Comp Engn, Halifax, NS B3J 2X4, Canada

来源：

PATTERN RECOGNITION | 2010年 / 43卷 / 06期

基金：

中国国家自然科学基金;

关键词：

PCNN; Image fusion; Focus measure;

D O I：

10.1016/j.patcog.2010.01.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：2003 / 2016

页数：14

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