MambaDFuse, a novel Mamba-based model, effectively and efficiently integrates complementary information from different modalities to generate high-quality fused images that enhance scene representation and facilitate downstream visual tasks.
DAE-Fuse, a novel two-phase discriminative autoencoder framework, generates sharp and natural fused images by introducing adversarial feature extraction and attention-guided cross-modality fusion.