  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.
