The paper introduces a novel low-light image enhancement method called CLIP-Fourier Guided Wavelet Diffusion (CFWD). CFWD leverages multimodal visual-language information in the frequency domain created by multiple wavelet transforms to guide the enhancement process. The method combines multi-scale supervision across different modalities to align image features with semantic features during wavelet diffusion, effectively bridging the gap between degraded and normal images. Additionally, a Hybrid High Frequency Perception Module (HHPM) is constructed to enhance detailed features by combining wavelet and Fourier transforms. This module avoids the diversity confusion of the wavelet diffusion process by guiding the fine-grained structure recovery of the enhancement results, achieving favorable metric and perceptual outcomes. Extensive experiments on public benchmarks show that CFWD outperforms existing state-of-the-art methods in image quality and noise suppression. The project code is available at https://github.com/hejb8/CFWD.The paper introduces a novel low-light image enhancement method called CLIP-Fourier Guided Wavelet Diffusion (CFWD). CFWD leverages multimodal visual-language information in the frequency domain created by multiple wavelet transforms to guide the enhancement process. The method combines multi-scale supervision across different modalities to align image features with semantic features during wavelet diffusion, effectively bridging the gap between degraded and normal images. Additionally, a Hybrid High Frequency Perception Module (HHPM) is constructed to enhance detailed features by combining wavelet and Fourier transforms. This module avoids the diversity confusion of the wavelet diffusion process by guiding the fine-grained structure recovery of the enhancement results, achieving favorable metric and perceptual outcomes. Extensive experiments on public benchmarks show that CFWD outperforms existing state-of-the-art methods in image quality and noise suppression. The project code is available at https://github.com/hejb8/CFWD.