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    FusionINV generates infrared and visible images (IVF) that look like visible images. This novel method uses Stable Diffusion for training-free fusion, improving machine perception tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Infrared and visible images have distinct appearances, creating domain differences.
    • Existing infrared and visible image fusion (IVF) methods produce fused images with unique appearances, challenging downstream applications like pre-trained segmentation models.
    • Accurate quality assessment of fused images is difficult due to domain discrepancies.

    Purpose of the Study:

    • To propose a novel IVF method, FusionINV, that generates fused images with a visible-light appearance.
    • To enable direct application of fused images to existing machine systems without domain adaptation.
    • To achieve training-free fusion leveraging advanced image inversion techniques.

    Main Methods:

    • FusionINV utilizes a pre-trained Stable Diffusion (SD) model to invert infrared images into a noise feature space.
    • Visible image features guide the infrared image inversion process, injecting visible-style appearance.
    • A tailored fusion rule within the SD denoising process iteratively combines visible-style infrared and visible features.

    Main Results:

    • FusionINV produces fused images that align closely with the RGB distribution, appearing similar to visible images.
    • The method successfully embeds information from both infrared and visible images in the noise feature space.
    • The generated fused images are directly applicable to downstream machine systems.

    Conclusions:

    • FusionINV effectively addresses the domain gap in IVF by generating visible-domain fused images.
    • The training-free approach, powered by image inversion, offers a significant advancement in IVF.
    • Experimental results confirm FusionINV's superior performance in both human visual evaluation and machine perception tasks.