关键词: 图像融合, 引导滤波, 自适应双通道脉冲耦合神经网络（DCPCNN）, 非下采样剪切波变换

Abstract: Aiming at the infrared images and visible images fusion is easy to produce edge information missing and the target is not enough, this paper combines the edge-preserving characteristics of the guided filtering with the non-linear modulation characteristics of the Dual-Channel Pulse Coupled Neural Network（DCPCNN）, an infrared and visible image fusion algorithm based on improved guided filtering and DCPCNN is proposed. Firstly, this paper obtains the high and low frequency components by using the Non-Subsampled Shearlet Transform（NSST） multi-scale decomposition of the strictly registered source images. Secondly, the low frequency components of the image is obtained by using the modified spatial frequency as the external excitation of the dual-channel PCNN, at the same time, the average gradient of low frequency components are used to adjust the link strength adaptively. Moreover, for the high frequency components of the image fusion, this paper uses local average gradient and regional variance adaptive weighting, and then uses improved guided filtering for smoothing to achieve spatial consistency. Finally, the paper uses the NSST inverse transform method to fuse low and high frequency components to obtain a fused image. Two types of experimental results for typical background targets and complex background targets show that proposed method can effectively synthesize the superiority information of the image. The proposed method is superior to the methods based on classical curvelet transform, dual-tree complex wavelet transform, Non-Subsampled Contourlet Transform（NSCT） and NSST in the quantitative evaluation indexes such as average gradient, standard deviation, spatial frequency and correlation coefficient.

Key words: image fusion, guided filtering, adaptive Dual-Channel Pulse Coupled Neural Network（DCPCNN）, Non-Subsampled Shearlet Transform（NSST）