  Color plays an important role in human visual perception, reflecting the
spectrum of objects. However, the existing infrared and visible image fusion
methods rarely explore how to handle multi-spectral/channel data directly and
achieve high color fidelity. This paper addresses the above issue by proposing
a novel method with diffusion models, termed as Dif-Fusion, to generate the
distribution of the multi-channel input data, which increases the ability of
multi-source information aggregation and the fidelity of colors. In specific,
instead of converting multi-channel images into single-channel data in existing
fusion methods, we create the multi-channel data distribution with a denoising
network in a latent space with forward and reverse diffusion process. Then, we
use the the denoising network to extract the multi-channel diffusion features
with both visible and infrared information. Finally, we feed the multi-channel
diffusion features to the multi-channel fusion module to directly generate the
three-channel fused image. To retain the texture and intensity information, we
propose multi-channel gradient loss and intensity loss. Along with the current
evaluation metrics for measuring texture and intensity fidelity, we introduce a
new evaluation metric to quantify color fidelity. Extensive experiments
indicate that our method is more effective than other state-of-the-art image
fusion methods, especially in color fidelity.
