基于NSCT-PCNN变换的多传感器图像融合

针对同源和异源的多传感器图像的特征,提出了一种基于非下采样Contourlet变换(NSCT)和脉冲耦合神经网络(PCNN)的新的图像融合算法。首先,用NSCT对已配准的源图像进行分解,从而准确地提取出了二维和更高维的边缘纹理信息;其次,对低频子带系数采用区域方差进行了整合,从而得到融合图像的低频子带系数,而对高频子带系数提出了一种改进的基于PCNN的图像融合方法来确定融合图像的各带通子带系数;最后通过对所有子带系数进行NSCT逆变换,从而得到了融合图像。实验结果表明,该方法优于Mallat小波方法和传统的NSCT方法,有更好的视觉效果。

Multi-Sensor Image Fusion based on NSCT-PCNN Transform

With the character of homologous and heterologous muti-sensor images,a novel image fusion algorithm by NSCT-PCNN transform was proposed.Above all,the registered input images are decomposed by nonsubsampled contourlet transform(NSCT) and the edge textures of two-dimension or high dimension are accurately extracted. Then the improved pulse coupled neural network(PCNN) is applied to high frequent subband coefficients integration, while the regional variance integration rules are for the low-pass subband part. Finally,the fusion image is achieved by inverse NSCT on the above-mentioned subband coefficients.The simulation experiments show that compared with the result of Mallat wavelet transform and Contourlet transform algorithm,that of the proposed method have the better visual effect.