Brighten-and-Colorize: A Decoupled Network for Customized Low-Light
  Image Enhancement
Chenxi Wang Zhi Jin
Low-Light Image Enhancement (LLIE) aims to improve the perceptual quality of
an image captured in low-light conditions. Generally, a low-light image can be
divided into lightness and chrominance components. Recent advances in this area
mainly focus on the refinement of the lightness, while ignoring the role of
chrominance. It easily leads to chromatic aberration and, to some extent,
limits the diverse applications of chrominance in customized LLIE. In this
work, a ``brighten-and-colorize'' network (called BCNet), which introduces
image colorization to LLIE, is proposed to address the above issues. BCNet can
accomplish LLIE with accurate color and simultaneously enables customized
enhancement with varying saturations and color styles based on user
preferences. Specifically, BCNet regards LLIE as a multi-task learning problem:
brightening and colorization. The brightening sub-task aligns with other
conventional LLIE methods to get a well-lit lightness. The colorization
sub-task is accomplished by regarding the chrominance of the low-light image as
color guidance like the user-guide image colorization. Upon completion of model
training, the color guidance (i.e., input low-light chrominance) can be simply
manipulated by users to acquire customized results. This customized process is
optional and, due to its decoupled nature, does not compromise the structural
and detailed information of lightness. Extensive experiments on the commonly
used LLIE datasets show that the proposed method achieves both State-Of-The-Art
(SOTA) performance and user-friendly customization.