DWT-Based Multisource Image Fusion Using Spatial Frequency and Simplified Pulse Coupled Neural Network

Abstract

In this paper, we present a new discrete wavelet transform (DWT)-based multisource image fusion algorithm using spatial frequency and a simplified pulse coupled neural network model named spiking cortical model (SCM). The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Firstly, source images are decomposed into low frequency sub-bands and high frequency sub-bands by DWT. Secondly, considering human visual system characteristics, two different fusion rules are used to fuse low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network rather than inputting coefficients value directly, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. The effectiveness of the proposed algorithm is achieved by the comparison with existing fusion methods.