  Images obtained in real-world low-light conditions are not only low in
brightness, but they also suffer from many other types of degradation, such as
color distortion, unknown noise, detail loss and halo artifacts. In this paper,
we propose a Degradation-Aware Deep Retinex Network (denoted as DA-DRN) for
low-light image enhancement and tackle the above degradation. Based on Retinex
Theory, the decomposition net in our model can decompose low-light images into
reflectance and illumination maps and deal with the degradation in the
reflectance during the decomposition phase directly. We propose a
Degradation-Aware Module (DA Module) which can guide the training process of
the decomposer and enable the decomposer to be a restorer during the training
phase without additional computational cost in the test phase. DA Module can
achieve the purpose of noise removal while preserving detail information into
the illumination map as well as tackle color distortion and halo artifacts. We
introduce Perceptual Loss to train the enhancement network to generate the
brightness-improved illumination maps which are more consistent with human
visual perception. We train and evaluate the performance of our proposed model
over the LOL real-world and LOL synthetic datasets, and we also test our model
over several other frequently used datasets without Ground-Truth (LIME, DICM,
MEF and NPE datasets). We conduct extensive experiments to demonstrate that our
approach achieves a promising effect with good rubustness and generalization
and outperforms many other state-of-the-art methods qualitatively and
quantitatively. Our method only takes 7 ms to process an image with 600x400
resolution on a TITAN Xp GPU.

Image and Video Processing;Computer Vision and Pattern Recognition