Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform

Abstract

A novel image fusion technique based on NSST (non-subsampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images. NSST, as a new member of MGA (multi-scale geometric analysis) tools, possesses not only flexible direction features and optimal shift-invariance, but much better fusion performance and lower computational costs compared with several current popular MGA tools such as NSCT (non-subsampled contourlet transform). We specifically propose new rules for the fusion of low and high frequency sub-band coefficients of source images in the second step of the NSST-based image fusion algorithm. First, the source images are decomposed into different scales and directions using NSST. Then, the model of region average energy (RAE) is proposed and adopted to fuse the low frequency sub-band coefficients of the gray-scale visual light and infrared images. Third, the model of local directional contrast (LDC) is given and utilized to fuse the corresponding high frequency sub-band coefficients. Finally, the final fused image is obtained by using inverse NSST to all fused sub-images. In order to verify the effectiveness of the proposed technique, several current popular ones are compared over three different publicly available image sets using four evaluation metrics, and the experimental results demonstrate that the proposed technique performs better in both subjective and objective qualities.