脉冲耦合神经网络在图像融合中的应用研究  

Application of Pulse Coupled Neural Network in Image Fusion

作　　者：邓辉 王长龙 胡永江 张玉华 DENG Hui;WANG Chang-long;HU Yong-jiang;ZHANG Yu-hua(Department of UAV Engineering Campus of Army Engineering University in Shijiazhuang, Shijiazhuang 050003, China)

机构地区：[1]陆军工程大学石家庄校区无人机工程系

出　　处：《电光与控制》2019年第11期19-24,共6页Electronics Optics & Control

基　　金：国家自然科学基金(51307183)

摘　　要：脉冲耦合神经网络(PCNN)具有全局耦合性与脉冲同步发放等特点,可用于解决融合图像高频子带系数的选取不符合人眼视觉特性的问题。但PCNN在应用于多源图像融合的过程中存在着模型结构复杂、参数设置繁琐等问题,针对PCNN的模型结构,分析了模型优化的两类方法,并总结了PCNN应用于多源图像融合的一般规律,为PCNN更好地应用于多源图像融合提供了参考。The characteristics of global coupling and pulse synchronization of Pulse Coupled Neural Network(PCNN) can be used to solve the problem that the selection of high-frequency sub-band coefficients of fused images does not conform to human visual characteristics. However,PCNN has many problems in multi-source image fusion,such as complex model structure and complicated parameter setting. As to the model structure of PCNN,two kinds of methods for model optimization are analyzed,and the general law of PCNN when applied in multi-source image fusion is summarized,which provides a reference for better application of PCNN in multi-source image fusion.

关 键 词：多源图像融合 脉冲耦合神经网络 模型结构 模型优化 

分 类 号：O213.2[理学—概率论与数理统计]