  Image fusion integrates essential information from multiple images into a
single composite, enhancing structures, textures, and refining imperfections.
Existing methods predominantly focus on pixel-level and semantic visual
features for recognition, but often overlook the deeper text-level semantic
information beyond vision. Therefore, we introduce a novel fusion paradigm
named image Fusion via vIsion-Language Model (FILM), for the first time,
utilizing explicit textual information from source images to guide the fusion
process. Specifically, FILM generates semantic prompts from images and inputs
them into ChatGPT for comprehensive textual descriptions. These descriptions
are fused within the textual domain and guide the visual information fusion,
enhancing feature extraction and contextual understanding, directed by textual
semantic information via cross-attention. FILM has shown promising results in
four image fusion tasks: infrared-visible, medical, multi-exposure, and
multi-focus image fusion. We also propose a vision-language dataset containing
ChatGPT-generated paragraph descriptions for the eight image fusion datasets
across four fusion tasks, facilitating future research in vision-language
model-based image fusion. Code and dataset are available at
https://github.com/Zhaozixiang1228/IF-FILM.
