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基于 NSCT 和加速 NMF 的改进图像融合方法。

Improved image fusion method based on NSCT and accelerated NMF.

机构信息

College of Computer Science, China West Normal University, 1 Shida Road, Nanchong 637002, China.

出版信息

Sensors (Basel). 2012;12(5):5872-87. doi: 10.3390/s120505872. Epub 2012 May 7.

Abstract

In order to improve algorithm efficiency and performance, a technique for image fusion based on the Non-subsampled Contourlet Transform (NSCT) domain and an Accelerated Non-negative Matrix Factorization (ANMF)-based algorithm is proposed in this paper. Firstly, the registered source images are decomposed in multi-scale and multi-direction using the NSCT method. Then, the ANMF algorithm is executed on low-frequency sub-images to get the low-pass coefficients. The low frequency fused image can be generated faster in that the update rules for W and H are optimized and less iterations are needed. In addition, the Neighborhood Homogeneous Measurement (NHM) rule is performed on the high-frequency part to achieve the band-pass coefficients. Finally, the ultimate fused image is obtained by integrating all sub-images with the inverse NSCT. The simulated experiments prove that our method indeed promotes performance when compared to PCA, NSCT-based, NMF-based and weighted NMF-based algorithms.

摘要

为了提高算法的效率和性能,本文提出了一种基于非下采样轮廓波变换(NSCT)域和基于加速非负矩阵分解(ANMF)算法的图像融合技术。首先,利用 NSCT 方法对已配准的源图像进行多尺度、多方向分解。然后,在低频子图像上执行 ANMF 算法以获取低频系数。通过优化 W 和 H 的更新规则并减少迭代次数,可以更快地生成低频融合图像。此外,对高频部分执行邻域均匀度量(NHM)规则以获得带通系数。最后,通过逆 NSCT 将所有子图像集成得到最终的融合图像。仿真实验证明,与 PCA、基于 NSCT、基于 NMF 和基于加权 NMF 的算法相比,我们的方法确实可以提高性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e887/3386717/e22867374aaf/sensors-12-05872f1.jpg

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