Image Fusion Algorithm Using RBF Neural Networks

Authors:
Hong Zhang;Xiao-Nan Sun;Lei Zhao;Lei Liu
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China 130012 and State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin U ...;State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China 130012;State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China 130012;College of Computer Science and Technology, Jilin University, Changchun, China 130012
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

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Abstract

This paper presents the image fusion algorithm using Radial Basis Function (RBF) neural networks. The clustering of every original image pixel is obtained by RBF neural networks combined with nearest neighbor clustering method. For each cluster center, the membership of every fused image pixel is adopted as the weighting coefficient of the weighted strategy, which is used to obtain the fusion image. The membership is obtained by maximum rule. The original data set is chosen as the candidate set of nearest neighbor clustering algorithm, and the center set of hidden units are dynamically established. In this experiment, the fusion results of various widths of hidden unit are compared with the results obtained by self-organizing feature map (SOFM) neural networks method. The influence of the various widths is discussed in this paper. The experiment results show that the proposed method can achieve better performance of the fused image.