  Self-regularized low-light image enhancement does not require any
normal-light image in training, thereby freeing from the chains on paired or
unpaired low-/normal-images. However, existing methods suffer color deviation
and fail to generalize to various lighting conditions. This paper presents a
novel self-regularized method based on Retinex, which, inspired by HSV,
preserves all colors (Hue, Saturation) and only integrates Retinex theory into
brightness (Value). We build a reflectance estimation network by restricting
the consistency of reflectances embedded in both the original and a novel
random disturbed form of the brightness of the same scene. The generated
reflectance, which is assumed to be irrelevant of illumination by Retinex, is
treated as enhanced brightness. Our method is efficient as a low-light image is
decoupled into two subspaces, color and brightness, for better preservation and
enhancement. Extensive experiments demonstrate that our method outperforms
multiple state-of-the-art algorithms qualitatively and quantitatively and
adapts to more lighting conditions.
