Poisson Reconstruction-Based Fusion of Infrared and Visible Images via Saliency Detection

Abstract

Saliency-based methods have been widely used in the fusion of infrared (IR) and visible (VIS) images, which can highlight the salient object region and preserve the detailed background information simultaneously. However, most existing methods ignore the salient information in the VIS image or they fail to highlight the boundaries of objects, which makes the final saliency map incomplete and the edges of the object blurred. To address the above-mentioned issues, we propose a novel IR and VIS images' fusion algorithm based on the Poisson reconstruction and saliency detection using the Dempster-Shafer (DS) theory. In detail, we mix the gradient using a mask map derived from the saliency map, which could avoid low contrast and halo effects in the results. Besides, both the intensity saliency of the IR image and the structural saliency of all source images are considered by DS to suppress some noise in the IR image. Thus, we could obtain smooth object contours and enhance the edge information of the salient region. Moreover, we also propose a novel probability mass function to calculate the probabilistic map in the process of applying DS to decrease the error from manually assigning the prior probability. Finally, the extensive qualitative and quantitative experiments have demonstrated the advantages and effectiveness of our method compared with other nine state-of-the-art IR and VIS image fusion methods.

Keywords

• Image fusion
• object detection
• context
• image reconstruction