A Dual-branch Progressive Network with spatial-frequency constraint for image fusion

Image fusion aims to integrate complementary information from source images to enhance the quality of fused representations. Most existing methods primarily impose pixel-level constraints in the spatial domain, which limits their ability to preserve frequency domain information. Furthermore, single-branch networks typically process source image features uniformly, which hinders cross-modal feature consideration. To address these challenges, we propose a Dual-branch Progressive Network (DPNet) for image fusion. First, a global feature fusion branch is constructed to enhance the extraction of long-range dependencies. This branch promotes global feature interaction through a Global Context Awareness (GCA) module. Subsequently, a local feature fusion branch is designed to extract local information from source images, which comprises multiple Local Feature Attention (LFA) modules to capture valuable local features. Additionally, to preserve both frequency and spatial domain information, we integrate two loss functions that jointly optimize feature retention in both domains. Experimental results on five datasets demonstrate that DPNet surpasses state-of-the-art fusion models both qualitatively and quantitatively. These findings validate its effectiveness for practical applications in military surveillance, environmental monitoring and medical imaging. The code is available at https://github.com/zenghui11/DPNet.