  Images captured under low-light conditions often suffer from (partially) poor
visibility. Besides unsatisfactory lightings, multiple types of degradations,
such as noise and color distortion due to the limited quality of cameras, hide
in the dark. In other words, solely turning up the brightness of dark regions
will inevitably amplify hidden artifacts. This work builds a simple yet
effective network for \textbf{Kin}dling the \textbf{D}arkness (denoted as
KinD), which, inspired by Retinex theory, decomposes images into two
components. One component (illumination) is responsible for light adjustment,
while the other (reflectance) for degradation removal. In such a way, the
original space is decoupled into two smaller subspaces, expecting to be better
regularized/learned. It is worth to note that our network is trained with
paired images shot under different exposure conditions, instead of using any
ground-truth reflectance and illumination information. Extensive experiments
are conducted to demonstrate the efficacy of our design and its superiority
over state-of-the-art alternatives. Our KinD is robust against severe visual
defects, and user-friendly to arbitrarily adjust light levels. In addition, our
model spends less than 50ms to process an image in VGA resolution on a 2080Ti
GPU. All the above merits make our KinD attractive for practical use.
