  Low-light images, i.e. the images captured in low-light conditions, suffer
from very poor visibility caused by low contrast, color distortion and
significant measurement noise. Low-light image enhancement is about improving
the visibility of low-light images. As the measurement noise in low-light
images is usually significant yet complex with spatially-varying
characteristic, how to handle the noise effectively is an important yet
challenging problem in low-light image enhancement. Based on the Retinex
decomposition of natural images, this paper proposes a deep learning method for
low-light image enhancement with a particular focus on handling the measurement
noise. The basic idea is to train a neural network to generate a set of
pixel-wise operators for simultaneously predicting the noise and the
illumination layer, where the operators are defined in the bilateral space.
Such an integrated approach allows us to have an accurate prediction of the
reflectance layer in the presence of significant spatially-varying measurement
noise. Extensive experiments on several benchmark datasets have shown that the
proposed method is very competitive to the state-of-the-art methods, and has
significant advantage over others when processing images captured in extremely
low lighting conditions.
