Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the characteristics of frequency domain information and diffusion models. WF-Diff consists of two detachable networks: Wavelet-based Fourier information interaction network (WFI2-net) and Frequency Residual Diffusion Adjustment Module (FRDAM). With our full exploration of the frequency domain information, WFI2-net aims to achieve preliminary enhancement of frequency information in the wavelet space. Our proposed FRDAM can further refine the high- and low-frequency information of the initial enhanced images, which can be viewed as a plug-and-play universal module to adjust the detail of the underwater images. With the above techniques, our algorithm can show SOTA performance on real-world underwater image datasets, and achieves competitive performance in visual quality.

翻译：水下图像会受到复杂且多样的退化影响，不可避免地降低了水下视觉任务的效果。然而，大多数方法主要在图像的原始像素空间中操作，限制了对水下图像频率特征的探索，导致深度模型在生成高质量图像时未能充分利用其表征能力。本文提出了一种名为WF-Diff的新型水下图像增强框架，旨在充分利用频域信息和扩散模型的特性。WF-Diff由两个可分离网络组成：基于小波的傅里叶信息交互网络和频率残差扩散调整模块。通过充分探索频域信息，WFI2-net旨在小波空间中实现频率信息的初步增强。我们提出的FRDAM可以进一步优化初始增强图像的高低频信息，该模块可作为即插即用的通用模块，用于调整水下图像的细节。借助上述技术，我们的算法在真实水下图像数据集上展现了最先进的性能，并在视觉质量方面达到了具有竞争力的表现。