Infrared and Visible Image Fusion Based on Nonparametric Segmentation

Abstract

Image fusion is a process of combining an image sequence of the same scene into a single image for better human perception & targeting. The thermal energy reflected from outstanding objects under poor lighting conditions and visible information that yield spatial details needs to be fused for improving the performance of surveillance systems. In this paper, we present a fusion technique that is helpful in surveillance systems for detecting targets when the background and the targets are of the same color. A nonparametric segmentation based weight map computation technique is proposed to extract target details from infrared (IR) imagery. The optimal threshold based on local features is selected automatically for target detection. With this, the extracted salient information of targets is blended to visible image without introducing distortions. The main advantage of the new technique is that it is based on a single-scale binary map (SSBM) fusion approach. The binary weight maps are computed for the fusion of separable IR target with visible imagery. An extension to IR and visible color image fusion is also suggested for target localization. Several simulation results are demonstrated for different data sets to support the validity of the proposed technique