Multi-scale infrared and visible image fusion framework based on dual partial differential equations

Image fusion aims to integrate multiple images that contain complementary information into a single image using an appropriate strategy. The fusion of infrared (IR) and visible (VIS) images can effectively integrate thermal radiation and texture information, which fuses foreground and background information into a fused image. An effective image fusion framework should transfer most of the information from the IR and VIS images to the fused image while minimizing artifacts. This paper introduces a novel multi-scale decomposition framework named Dual PDEs, which decomposes the source images into multiple layers using two different types of partial differential equations (PDEs) and fuses the feature representations at each scale layer separately with an adaptive fusion scheme. The target image is obtained from the multi-scale fused layers. Qualitative and quantitative evaluations show that the proposed framework effectively highlights the prominent areas with fewer artifacts, more explicit boundaries, and better visual effects, which outperforms existing fusion methods.