In this paper, we propose a novel underwater image enhancement method, by
utilizing the multi-guided diffusion model for iterative enhancement. Unlike
other image enhancement tasks, underwater images suffer from the unavailability
of real reference images. Although existing works exploit synthetic images,
manually selected well-enhanced images as reference images, to train
enhancement networks, their enhancement performance always comes with
subjective preferences that are inherited from the manual selection. To address
this issue, we also use the image synthesis strategy, but the synthetic images
derive from in-air natural images degraded into corresponding underwater
images, guided by the underwater domain. Based on this strategy, the diffusion
model can learn the prior knowledge of image enhancement from the underwater
degradation domain to the real in-air natural domain. However, it is inevitable
to fine-tune the model to suit downstream tasks, and this may erase the prior
knowledge. To mitigate this, we combine the prior knowledge from the in-air
natural domain with Contrastive Language-Image Pretraining (CLIP) to train a
classifier for controlling the diffusion model generation process. Moreover,
for image enhancement tasks, we find that the image-to-image diffusion model
and the CLIP-Classifier mainly act in the high-frequency region during the
fine-tuning process. Therefore, we propose a fast fine-tuning strategy focusing
on the high-frequency region, which can be up to 10 times faster than the
traditional strategy. Extensive experiments demonstrate that our method,
abbreviated as CLIP-UIE, exhibit a more natural appearance.

我们提出了一种新颖的水下图像增强方法，通过利用多导向扩散模型进行迭代增强。我们使用图像合成策略并结合对比语言 - 图像预训练（CLIP）来训练一个控制扩散模型生成过程的分类器，在高频区域提出一种快速微调策略，结果表明我们的方法具有更自然的外观。