HDF-Net: Hierarchical Dual-Branch Feature Extraction Fusion Network for Infrared and Visible Image Fusion.

To enhance scene perception and comprehension, infrared and visible image fusion (IVIF) integrates complementary data from two modalities. However, many existing methods fail to explicitly separate modality-specific and modality-shared features, which compromises fusion quality. To surmount this constraint, we introduce a novel hierarchical dual-branch fusion (HDF-Net) network. The network decomposes the source images into low-frequency components, which capture shared structural information, and high-frequency components, which preserve modality-specific details. Remarkably, we propose a pin-wheel-convolutional transformer (PCT) module that integrates local convolutional processing with directional attention to improve low-frequency feature extraction, thereby enabling more robust global-local context modeling. We subsequently introduce a hierarchical feature refinement (HFR) block that adaptively integrates multiscale features using kernel-based attention and dilated convolutions, further improving fusion accuracy. Extensive experiments on four public IVIF datasets (MSRS, TNO, RoadScene, and M3FD) demonstrate the high competitiveness of HDF-Net against 12 state-of-the-art methods. On the RoadScene dataset, HDF-Net achieves top performance across six key metrics-EN, SD, AG, SF, SCD, and SSIM-surpassing the second-best method by 0.67%, 1.85%, 17.67%, 5.26%, 3.33%, and 1.01%, respectively. These findings verify the generalization and efficacy of HDF-Net in practical IVIF scenarios.