Denosing diffusion model, as a generative model, has received a lot of
attention in the field of image generation recently, thanks to its powerful
generation capability. However, diffusion models have not yet received
sufficient research in the field of image fusion. In this article, we introduce
diffusion model to the image fusion field, treating the image fusion task as
image-to-image translation and designing two different conditional injection
modulation modules (i.e., style transfer modulation and wavelet modulation) to
inject coarse-grained style information and fine-grained high-frequency and
low-frequency information into the diffusion UNet, thereby generating fused
images. In addition, we also discussed the residual learning and the selection
of training objectives of the diffusion model in the image fusion task.
Extensive experimental results based on quantitative and qualitative
assessments compared with benchmarks demonstrates state-of-the-art results and
good generalization performance in image fusion tasks. Finally, it is hoped
that our method can inspire other works and gain insight into this field to
better apply the diffusion model to image fusion tasks. Code shall be released
for better reproducibility.

本篇文章将去噪扩散模型引入图像融合领域，将融合任务作为图像到图像的转换，设计两种不同的条件注入模块，即风格转换调制和小波调制，注入粗粒度风格信息和细粒度高低频信息来生成融合图像，并讨论去噪扩散模型在图像融合中的残差学习和目标函数选择。基于定量和定性评估的广泛实验结果表明，在图像融合任务中具有最先进的结果和良好的泛化性能，希望本文可以激发其他工作的灵感，并深入了解该领域，以更好地将扩散模型应用于图像融合任务。