基于NSCT域改进型NMF的图像融合方法

Technique for image fusion based on non-subsampled contourlet transform domain improved NMF

针对多传感器图像融合问题,提出一种基于非下采样轮廓波变换域改进型非负矩阵分解的图像融合方法.首先,采用非下采样轮廓波变换对源图像进行多尺度、多方向稀疏分解;然后对低频子图像采用改进型非负矩阵分解方法进行融合,该过程无需对W和H进行随机化便可快速生成低频融合图像,高频子图像则采用自适应unit-fast-linking脉冲耦合神经网络模型进行融合;最后利用非下采样轮廓波逆变换合成各子图像即可得到最终融合图像.仿真实验验证了该方法的有效性.

To overcome the problem of multi-sensor image fusion,a technique for image fusion based on non-subsampled contourlet transform（NSCT） domain improved nonnegative matrix factorization（NMF） is presented. Firstly,by using NSCT,multi-scale and multi-direction sparse decompositions of source images are performed. Then,an improved NMF technique is utilized to complete the fusion of low-frequency sub-images.The low-frequency fused image can be produced fast by the process which does not involve the randomization of the vectors W and H at all,in addition,the fusion course of high-frequency sub-images can be dealt with by use of the model of adaptive unit-fast-linking pulse coupled neural network（AUFLPCNN）.Finally,the ultimate fused image can be obtained by synthesizing all sub-images with inverse NSCT.The simulated experiments show that the technique is effective.