  Deep learning-based methods for low-light image enhancement typically require
enormous paired training data, which are impractical to capture in real-world
scenarios. Recently, unsupervised approaches have been explored to eliminate
the reliance on paired training data. However, they perform erratically in
diverse real-world scenarios due to the absence of priors. To address this
issue, we propose an unsupervised low-light image enhancement method based on
an effective prior termed histogram equalization prior (HEP). Our work is
inspired by the interesting observation that the feature maps of histogram
equalization enhanced image and the ground truth are similar. Specifically, we
formulate the HEP to provide abundant texture and luminance information.
Embedded into a Light Up Module (LUM), it helps to decompose the low-light
images into illumination and reflectance maps, and the reflectance maps can be
regarded as restored images. However, the derivation based on Retinex theory
reveals that the reflectance maps are contaminated by noise. We introduce a
Noise Disentanglement Module (NDM) to disentangle the noise and content in the
reflectance maps with the reliable aid of unpaired clean images. Guided by the
histogram equalization prior and noise disentanglement, our method can recover
finer details and is more capable to suppress noise in real-world low-light
scenarios. Extensive experiments demonstrate that our method performs favorably
against the state-of-the-art unsupervised low-light enhancement algorithms and
even matches the state-of-the-art supervised algorithms.
