Skip to Main Content
In this paper, a new image fusion algorithm based on wavelet packet analysis (WPA) and pulse-coupled neural networks (PCNN) is presented. Firstly a multi-scale decomposition of each source image is performed by wavelet packet transform (WPT), and then the PCNN is used to make an intelligent fusion decision to obtain the fusion coefficients in all wavelet packet fields. Finally an inverse WPT based on the new fused coefficients is taken to reconstruct fusion image. The highlight of the new algorithm is to utilize the global feature of source images because the PCNN has the global couple and pulse synchronization characteristics. It accords with the physiological characteristic of human visual neural system. Series of experiments are performed on comparing the new algorithm with some other existing fusion methods. The experimental results show that the new algorithm is very effective and provides a better performance in fusing multi-source images.
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on (Volume:9 )
Date of Conference: 18-21 Aug. 2005