Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwhile, limited by poor lighting and content guidance, existing zero-shot methods cannot handle unknown severe degradation well. To address this problem, we will propose a new zero-shot low-light enhancement method to compensate for the lack of light and structural information in the diffusion sampling process by effectively combining the wavelet and Fourier frequency domains to construct rich a priori information. The key to the inspiration comes from the similarity between the wavelet and Fourier frequency domains: both light and structure information are closely related to specific frequency domain regions, respectively. Therefore, by transferring the diffusion process to the wavelet low-frequency domain and combining the wavelet and Fourier frequency domains by continuously decomposing them in the inverse process, the constructed rich illumination prior is utilised to guide the image generation enhancement process. Sufficient experiments show that the framework is robust and effective in various scenarios. The code will be available at: \href{https://github.com/hejh8/Joint-Wavelet-and-Fourier-priors-guided-diffusion}{https://github.com/hejh8/Joint-Wavelet-and-Fourier-priors-guided-diffusion}.

翻译：由于现实世界配对数据集的单一性以及低光照环境的复杂性，这导致监督方法缺乏一定程度的场景泛化能力。同时，受限于光照条件差和内容引导不足，现有的零样本方法无法很好地处理未知的严重退化问题。为解决此问题，我们提出一种新的零样本低光照增强方法，通过有效结合小波与傅里叶频域来构建丰富的先验信息，以弥补扩散采样过程中光照与结构信息的不足。该灵感的关键来源于小波与傅里叶频域的相似性：光照信息与结构信息分别与特定的频域区域密切相关。因此，通过将扩散过程迁移至小波低频域，并在逆过程中通过持续分解的方式结合小波与傅里叶频域，利用所构建的丰富光照先验来引导图像生成增强过程。充分的实验表明，该框架在各种场景下均具有鲁棒性和有效性。代码将发布于：\href{https://github.com/hejh8/Joint-Wavelet-and-Fourier-priors-guided-diffusion}{https://github.com/hejh8/Joint-Wavelet-and-Fourier-priors-guided-diffusion}。