  Understanding illumination and reducing the need for supervision pose a
significant challenge in low-light enhancement. Current approaches are highly
sensitive to data usage during training and illumination-specific
hyper-parameters, limiting their ability to handle unseen scenarios. In this
paper, we propose a new zero-reference low-light enhancement framework
trainable solely with normal light images. To accomplish this, we devise an
illumination-invariant prior inspired by the theory of physical light transfer.
This prior serves as the bridge between normal and low-light images. Then, we
develop a prior-to-image framework trained without low-light data. During
testing, this framework is able to restore our illumination-invariant prior
back to images, automatically achieving low-light enhancement. Within this
framework, we leverage a pretrained generative diffusion model for model
ability, introduce a bypass decoder to handle detail distortion, as well as
offer a lightweight version for practicality. Extensive experiments demonstrate
our framework's superiority in various scenarios as well as good
interpretability, robustness, and efficiency. Code is available on our project
homepage: http://daooshee.github.io/QuadPrior-Website/
