Texture-Content Dual Guided Network for Visible and Infrared Image Fusion

The preservation and enhancement of texture information is crucial for the fusion of visible and infrared images. However, most current deep neural network (DNN)-based methods ignore the differences between texture and content, leading to unsatisfactory fusion results. To further enhance the quality of fused images, we propose a texture-content dual guided (TCDG-Net) network, which produces the fused image by the guidance inferred from source images. Specifically, a texture map is first estimated jointly by combining the gradient information of visible and infrared images. Then, the features learned by the shallow feature extraction (SFE) module are enhanced with the guidance of the texture map. To effectively model the texture information in the long-range dependencies, we design the texture-guided enhancement (TGE) module, in which the texture-guided attention mechanism is utilized to capture the global similarity of the texture regions in source images. Meanwhile, we employ the content-guided enhancement (CGE) module to refine the content regions in the fused result by utilizing the complement of the texture map. Finally, the fused image is generated by adaptively integrating the enhanced texture and content information. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed TCDG-Net in terms of qualitative and quantitative evaluations. Besides, the fused images generated by our proposed TCDG-Net also show better performance in downstream tasks, such as objection detection and semantic segmentation.