An Infrared and Visible Image Fusion Method Based on Non-Subsampled Contourlet Transform and Joint Sparse Representation

Abstract

In conventional fusion methods based on NonSubsampled Contourlet Transform (NSCT), low-frequency subband coefficient of an image fails to express sparsely the image's low-frequency information, not in favor of extracting source image features. To address this issue, an infrared and visible image fusion method based on NSCT and joint sparse representation (JSR) was proposed, in which, JSR transform of the image's low-frequency information is conducive to improving sparsity of low-frequency subband containing main energy of the image, as to high-frequency information, use of feature product as a fusion rule is beneficial to extract detail feature of the source image. Experimental result indicates that, compared with conventional multiscale transform-based DWT, NSCT-based fusion method and sparse representation-based SR and JSR algorithms, the method in this paper achieved better fusion effect, capable of keeping target information of the infrared image and background detail information (edge, texture, etc.) of the visible image better.