  Recently, researchers have proposed various deep learning methods to
accurately detect infrared targets with the characteristics of indistinct shape
and texture. Due to the limited variety of infrared datasets, training deep
learning models with good generalization poses a challenge. To augment the
infrared dataset, researchers employ data augmentation techniques, which often
involve generating new images by combining images from different datasets.
However, these methods are lacking in two respects. In terms of realism, the
images generated by mixup-based methods lack realism and are difficult to
effectively simulate complex real-world scenarios. In terms of diversity,
compared with real-world scenes, borrowing knowledge from another dataset
inherently has a limited diversity. Currently, the diffusion model stands out
as an innovative generative approach. Large-scale trained diffusion models have
a strong generative prior that enables real-world modeling of images to
generate diverse and realistic images. In this paper, we propose Diff-Mosaic, a
data augmentation method based on the diffusion model. This model effectively
alleviates the challenge of diversity and realism of data augmentation methods
via diffusion prior. Specifically, our method consists of two stages. Firstly,
we introduce an enhancement network called Pixel-Prior, which generates highly
coordinated and realistic Mosaic images by harmonizing pixels. In the second
stage, we propose an image enhancement strategy named Diff-Prior. This strategy
utilizes diffusion priors to model images in the real-world scene, further
enhancing the diversity and realism of the images. Extensive experiments have
demonstrated that our approach significantly improves the performance of the
detection network. The code is available at
https://github.com/YupeiLin2388/Diff-Mosaic
