DDFNet: A Dual-Stream Decoupled Feature Alignment Network for Multimodal Medical Image Fusion

Multimodal Medical Image Fusion (MMIF) aims to exploit the correlation and complementarity of different imaging modalities to integrate cross-modal information and provide more potential support for clinical diagnosis. However, effectively preserving both shallow and deep features of each modality while integrating multi-source representations remains challenging, often leading to blurred edges and loss of fine details in fused images. To address this issue, this paper proposes a novel Dual-stream Decoupled Feature parallel alignment fusion network (DDFNet). Built upon an autoencoder (AE) framework, the encoder adopts a dual-branch multi-scale design, consisting of a Detail Feature Extraction Module (DFEM) and a Global Context Feature Extraction Module (GCFEM). These two parallel branches leverage the complementary advantages of CNNs and Transformers to capture local details and global contextual dependencies, respectively, while operating independently without feature interference. In addition, an Improved Multi-scale Cross-alignment and Spatial Attention Fusion module (IMSC-SAF) is introduced to enhance feature integration. It performs complementary feature alignment and cross-attention to strengthen multiscale interactions, while a spatial attention mechanism highlights spatially salient regions. The decoder then reconstructs the fused representation and generates the final fused image. Extensive experiments on publicly available datasets from Harvard Medical School demonstrate that the proposed method achieves competitive fusion performance. In particular, DDFNet consistently obtains the highest MI, VIF, and SSIM values across the evaluated datasets while maintaining competitive results in the remaining objective metrics. Qualitative comparisons further demonstrate that the proposed method effectively preserves complementary anatomical and functional information, producing visually balanced fusion results.