Infrared and Visible Image Fusion Based on Region of Interest Detection and Nonsubsampled Contourlet Transform

In order to enhance the contrast of the fused image and reduce the loss of fine details in the process of image fusion, a novel fusion algorithm of infrared and visible images is proposed. First of all, regions of interest (RoIs) are detected in two original images by using saliency map. Then, nonsubsampled contourlet transform (NSCT) on both the infrared image and the visible image is performed to get a low-frequency sub-band and a certain amount of high-frequency sub-bands. Subsequently, the coefficients of all sub-bands are classified into four categories based on the result of RoI detection: the region of interest in the low-frequency sub-band (LSRoI), the region of interest in the high-frequency sub-band (HSRoI), the region of non-interest in the low-frequency sub-band (LSNRoI) and the region of non-interest in the high-frequency sub-band (HSNRoI). Fusion rules are customized for each kind of coefficients and fused image is achieved by performing the inverse NSCT to the fused coefficients. Experimental results show that the fusion scheme proposed in this paper achieves better effect than the other fusion algorithms both in visual effect and quantitative metrics.