Guided filter random walk and improved spiking cortical model based image fusion method in NSST domain

Abstract

Image fusion has become a hot issue in the field of information processing. In this paper, a novel image fusion method based on guided filter random walk and improved spiking cortical model (ISCM) in non-subsampled shearlet transform (NSST) is presented. The core process is composed of three steps. Firstly, the source images to be fused are decomposed into the low-frequency parts and high-frequency parts via NSST. Then, two models including guided filter and random walk are combined to complete the fusion of low-frequency sub-images. As for the fusion of the high-frequency parts, the traditional spiking cortical model is improved to be ISCM to capture and fuse the details information of the source images. Finally, the fused image can be obtained by inverse NSST. In order to verify the effectiveness of the proposed method, lots of datasets with different categories are selected as the source images to conduct the simulation experiments. Experimental results demonstrate that the proposed method own obvious superiorities over the representative ones in terms of both subjective visual performance and objective evaluation data.