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非降采样Contourlet域内空间频率激励的PCNN图像融合算法
引用本文:屈小波,闫敬文,肖弘智,朱自谦.非降采样Contourlet域内空间频率激励的PCNN图像融合算法[J].自动化学报,2008,34(12):1508-1514.
作者姓名:屈小波  闫敬文  肖弘智  朱自谦
作者单位:1.厦门大学通信工程系 厦门 361005
基金项目:国家自然科学基金,国家航天科学基金
摘    要:Nonsubsampled contourlet transform (NSCT) 能够提供灵活的多分辨率分解, 具有各向异性和图像方向性扩展特点. 与原始的Contourlet相比, 它是频移不变的, 能有效克服Contourlet变换中的伪吉布斯现象. 脉冲耦合神经网络(Pulse Coupled Neural Networks-PCNN)是一种具有视觉生理学基础的神经网络, 具有全局耦合和神经元同步脉冲发放特性, 已经被成功应用于图像处理和图像融合中. 本文将NSCT与PCNN结合起来, 充分利用二者的特性. 以NSCT变换域内系数的空间频率激励PCNN神经元, 选择点火次数大的系数作为融合图像的系数, 经NSCT反变换得到融合图像. 实验表明, 本文算法无论在视觉效果还是客观评价指标上, 都优于基于小波变换、基于Contourlet变换、基于PCNN和基于Contourlet-PCNN等融合算法.

关 键 词:图像融合    Contourlet变换    脉冲耦合神经网络    空间频率    小波变换
收稿时间:2007-11-29
修稿时间:2008-3-29

Image Fusion Algorithm Based on Spatial Frequency-motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain
QU Xiao-Bo,YAN Jing-Wen,XIAO Hong-Zhi,ZHU Zi-Qian.Image Fusion Algorithm Based on Spatial Frequency-motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain[J].Acta Automatica Sinica,2008,34(12):1508-1514.
Authors:QU Xiao-Bo  YAN Jing-Wen  XIAO Hong-Zhi  ZHU Zi-Qian
Affiliation:1.Department of Communication Engineering, Xiamen University, Xiamen 361005, P.R.China;2.Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, P.R.China;3.Research Institute of Chinese Radar Electronic Equipment, Wuxi 214063, P.R.China
Abstract:Nonsubsampled contourlet transform(NSCT)provides flexible multiresolution,anisotropy,and directional expansion for images.Compared with the original contourlet transform,it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities.Pulse coupled neural networks(PCNN)is a visual cortex-inspired neural network and characterized by the global coupling and pulse synchronization of neurons.It has been proven suitable for image processing and successfully employed in image fusion.In this paper,NSCT is associated with PCNN and used in image fusion to make full use of the characteristics of them. Spatial frequency in NSCT domain is input to motivate PCNN and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image.Experimental results demonstrate that the proposed algorithm outperforms typical wavelet-based, contourlet-based,PCNN-based,and contourlet-PCNN-based fusion algorithms in terms of objective criteria and visual appearance.
Keywords:Contourlet  pulse coupled neural networks(PCNN)  wavelet  image fusion  multiscale transform
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