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
this https URL

通过 CLIP-Fourier Guided Wavelet Diffusion 方法的混合频率域空间，我们设计了一个通过小波变换实现有效图像增强的引导网络，能够恢复图像的细粒度结构并避免多样性混淆。经过大量定量和定性实验证明，我们的方法胜过现有的最先进方法，并更好地再现了类似于正常图像的图像。