  Low-light image enhancement techniques have significantly progressed, but
unstable image quality recovery and unsatisfactory visual perception are still
significant challenges. To solve these problems, we propose a novel and robust
low-light image enhancement method via CLIP-Fourier Guided Wavelet Diffusion,
abbreviated as CFWD. Specifically, CFWD leverages multimodal visual-language
information in the frequency domain space created by multiple wavelet
transforms to guide the enhancement process. Multi-scale supervision across
different modalities facilitates the alignment of image features with semantic
features during the wavelet diffusion process, effectively bridging the gap
between degraded and normal domains. Moreover, to further promote the effective
recovery of the image details, we combine the Fourier transform based on the
wavelet transform and construct a Hybrid High Frequency Perception Module
(HFPM) with a significant perception of the detailed features. This module
avoids the diversity confusion of the wavelet diffusion process by guiding the
fine-grained structure recovery of the enhancement results to achieve
favourable metric and perceptually oriented enhancement. Extensive quantitative
and qualitative experiments on publicly available real-world benchmarks show
that our approach outperforms existing state-of-the-art methods, achieving
significant progress in image quality and noise suppression. The project code
is available at https://github.com/hejh8/CFWD.
