摘要
提出一种基于非下采样contourlet变换(NSCT)与脉冲耦合神经网络(PCNN)的图像融合算法。该方法首先利用非下采样contourlet变换对输入图像进行多尺度分解、多方向稀疏分解,准确捕获图像中的高维奇异信息,然后利用脉冲耦合神经网络的同步激发特性确定融合规则,选取融合系数,提高融合性能。实验结果表明,算法比小波变换、contourlet变换有更好的融合性能。
A fusion algorithm based on Nonsubsampled Contourlet Transform(NSCT)and Pulse Coupled Neural Network(PCNN)was proposed.By using NSCT,the input images were decomposed into a number of sub-images with various scales and directional features.Then,based on PCNN,a fusion rule was given.The fused coefficients could be generated by PCNN-based fusion rule and the fused image was obtained by performing the inverse NSCT to fused coefficients.The experimental results show that the fusion method is more effective than wavelet transform and contourlet transform.
出处
《计算机应用》
CSCD
北大核心
2008年第S2期164-167,共4页
journal of Computer Applications
基金
总装备部科研基金资助项目
