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

摘要

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.