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, we design a guided network with a multiscale visual language in the frequency domain based on the wavelet transform to achieve effective image enhancement iteratively. In addition, we combine the advantages of Fourier transform in detail perception to construct a hybrid frequency domain space with significant perceptual capabilities(HFDPM). This operation guides wavelet diffusion to recover the fine-grained structure of the image and avoid diversity confusion. Extensive quantitative and qualitative experiments on publicly available real-world benchmarks show that our method outperforms existing state-of-the-art methods and better reproduces images similar to normal images. Code is available at https://github.com/He-Jinhong/CFWD.

翻译：低光图像增强技术已取得显著进展，但图像质量恢复不稳定及视觉感知效果不理想仍是重大挑战。为解决这些问题，我们提出一种基于CLIP-傅里叶引导小波扩散的新型鲁棒低光图像增强方法，简称CFWD。具体而言，我们基于小波变换设计了一个具有多尺度视觉语言的频率域引导网络，实现了迭代式有效图像增强。此外，我们结合傅里叶变换在细节感知方面的优势，构建了具有显著感知能力的混合频率域空间（HFDPM）。该操作可引导小波扩散恢复图像的细粒度结构，并避免多样性混淆。在公开真实世界基准数据集上的大量定量与定性实验表明，我们的方法优于现有最优方法，能够更好地重建与正常图像相似的结果。代码已开源至https://github.com/He-Jinhong/CFWD。