Issue Date: 6-7 March 2010rnrntOn page(s): rnt27rnttrn- 30rnrnrnLocation: Wuhan, ChinarnrnPrint ISBN: 978-1-4244-6388-6rnrnrnrnttrnDigital Object Identifier: href='http://dx.doi.org/10.1109/ETCS.2010.61' target='_blank'>10.1109/ETCS.2010.61 rnrnDate of Current Version: trnrnt2010-05-06 14:33:53.0rnrnt rntt class="body-text">rntname="Abstract">>Abstractrn>The principles and features of nonsubsampled contourlet transform (NSCT) and pulse coupled neural networks (PCNN) are described in brief. Combining their characteristics, in NSCT domain, a new image fusion algorithm based on PCNN is proposed in this paper. Directional contrast and regional spatial frequency in NSCT domain is input to motivate PCNN and coefficients in NSCT domai;
Contrast; Image Fusion; Nonsubsampled Contourlet; pulse coupled neural networks (PCNN);
机译:基于脉冲耦合神经网络和非下采样Contourlet变换的红外与可见光图像融合
机译:基于非下采样Contourlet变换和显着性激励脉冲耦合神经网络的图像融合
机译:基于非下采样Contourlet变换和显着性激励脉冲耦合神经网络的图像融合
机译:基于脉冲耦合神经网络和非下采样Contourlet变换的图像融合算法
机译:一种新的基于混合小波的Contourlet变换的多峰图像融合方法。
机译:基于非下采样Contourlet变换和遗传算法的刚性图像配准
机译:基于动机脉冲耦合神经网络的多焦焦图像融合算法使用非基本剖面变换