FusionINV: A Diffusion-Based Approach for Multimodal Image Fusion

Infrared images exhibit a significantly different appearance compared to visible counterparts. Existing infrared and visible image fusion (IVF) methods fuse features from both infrared and visible images, producing a new "image" appearance not inherently captured by any existing device. From an appearance perspective, infrared, visible, and fused images belong to different data domains. This difference makes it challenging to apply fused images because their domain-specific appearance may be difficult for downstream systems, e.g., pre-trained segmentation models. Therefore, accurately assessing the quality of the fused image is challenging. To address those problem, we propose a novel IVF method, FusionINV, which produces fused images with an appearance similar to visible images. FusionINV employs the pre-trained Stable Diffusion (SD) model to invert infrared images into the noise feature space. To inject visible-style appearance information into the infrared features, we leverage the inverted features from visible images to guide this inversion process. In this way, we can embed all the information of infrared and visible images in the noise feature space, and then use the prior of the pre-trained SD model to generate visually friendly images that align more closely with the RGB distribution. Specially, to generate the fused image, we design a tailored fusion rule within the denoising process that iteratively fuses visible-style infrared and visible features. In this way, the fused image falls into the visible domain and can be directly applied to existing downstream machine systems. Thanks to advancements in image inversion, FusionINV can directly produce fused images in a training-free manner. Extensive experiments demonstrate that FusionINV achieves outstanding performance in both human visual evaluation and machine perception tasks. The code is available at https://github.com/erfect2020/FusionINV.