Infrared and Visual Image Fusion Based on NSST and Improved PCNN

Abstract

Aiming at the difference characteristics between infrared image target information and visible images detail information, this paper proposed a novel infrared and visible images fusion algorithm based on Non-subsampled Shearlet Transform (NSST). Firstly we obtain the high and low frequency components by using the NSST multi-scale decomposition of the strictly registered source images. Secondly, the low frequency components are fused by using the modified spatial frequency as the external excitation of the 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, we present an self-adaptive fusion rule algorithm based on local area variance and local area average gradient. Finally, this paper uses the NSST inverse transform method to fuse low and high frequency components to obtain a fused image. Experimental results show that the proposed method of image fusion can effectively integrate important information in infrared and visible images, and the fusion effect is better than the general image fusion methods based on NSCT and NSST.