From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion

Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a single fused image that preserves fine local details while maintaining globally consistent appearance. Most existing approaches build shared representations on 2D feature grids, which excel at modeling local structures but offer limited leverage over image-level appearance factors. To better optimize two objectives jointly, we redesign the shared representation by mapping inputs into a compact sequence of discrete 1D image tokens, and instantiate this design with TiTok as a lightweight tokenizer, decoupling the shared representation from fixed pixel locations and concentrating image-level attributes into a small set of global tokens. We propose Selective Token Editing (STE): we sparsely update/replace only a small set of critical shared tokens, providing a lightweight token-level mechanism to steer global appearance coherence while keeping the fusion backbone unchanged and avoiding complex loss designs. Experiments on multiple benchmarks show that our method delivers consistent, multi-metric improvements—enhancing global coherence and local fidelity simultaneously—and achieves the best overall performance under comprehensive evaluation.