A Novel Fusion Method of Infrared and Visible Images Based on Non-subsampled Contourlet Transform

摘要

This paper presents a novel infrared (IR) and visible images fusion methodology based on non-subsampled contourlet transform (NSCT). NSCT shows better performance compared with usual multi-scale decomposition for its multi-scale, shift invariance, multi-direction and efficient capture of geometric structures. The proposed fusion method uses NSCT for multiresolution decomposition of the source images. The low-pass NSCT adaptive fusion weights calculated from the IR source image's pixel statistical characteristics. The high frequency directional coefficients with max absolute value are the coefficients of the fusion NSCT high frequency. Experimental results conforms that the proposed method have better performance compared with DWT, compressed sensing based on DWT (CS-DWT), NSCT, NSCT based on spatial frequency motivated pulse coupled neural networks (SF-PCNN-NSCT) from visual effects and a list of fusion quality evaluation metrics.