  It is suggested that low-light image enhancement realizes one-to-many mapping
since we have different definitions of NORMAL-light given application scenarios
or users' aesthetic. However, most existing methods ignore subjectivity of the
task, and simply produce one result with fixed brightness. This paper proposes
a neural network for multi-level low-light image enhancement, which is
user-friendly to meet various requirements by selecting different images as
brightness reference. Inspired by style transfer, our method decomposes an
image into two low-coupling feature components in the latent space, which
allows the concatenation feasibility of the content components from low-light
images and the luminance components from reference images. In such a way, the
network learns to extract scene-invariant and brightness-specific information
from a set of image pairs instead of learning brightness differences. Moreover,
information except for the brightness is preserved to the greatest extent to
alleviate color distortion. Extensive results show strong capacity and
superiority of our network against existing methods.
