Feature-decoupled multi-scale Swin transformer for fusing infrared and polarization images

Infrared polarization image fusion aims to generate a single image that integrates complementary information from both modalities to enhance scene perception. However, this task is hindered by significant modality gaps, high noise levels in polarization images, and the difficulty of preserving fine details from both sources. To address these challenges, we propose an end-to-end network, the feature-decoupled multi-scale Swin transformer (FDMSFuse). The proposed network uses a multi-scale architecture to capture rich shallow features. Its core component, the mix Swin transformer layer, employs a symmetric shared-key-value attention mechanism for efficient cross-modal interaction. Furthermore, a feature decoupling loss based on a channel-correlation matrix promotes feature complementarity while the DySample module ensures high-quality detail reconstruction. Experiments on the public LDDRS dataset demonstrate that FDMSFuse significantly outperforms nine state-of-the-art methods, ranking first on seven of nine key metrics. Crucially, it improves normalized mutual information (NMI) by nearly 25% over the runner-up, while also achieving top scores for visual fidelity (VIF) and perceived aesthetic quality (NIMA). Qualitative results further confirm its superior performance in noise suppression, texture preservation, and small-target enhancement.