  Images captured under low-light conditions manifest poor visibility, lack
contrast and color vividness. Compared to conventional approaches, deep
convolutional neural networks (CNNs) perform well in enhancing images. However,
being solely reliant on confined fixed primitives to model dependencies,
existing data-driven deep models do not exploit the contexts at various spatial
scales to address low-light image enhancement. These contexts can be crucial
towards inferring several image enhancement tasks, e.g., local and global
contrast, brightness and color corrections; which requires cues from both local
and global spatial extent. To this end, we introduce a context-aware deep
network for low-light image enhancement. First, it features a global context
module that models spatial correlations to find complementary cues over full
spatial domain. Second, it introduces a dense residual block that captures
local context with a relatively large receptive field. We evaluate the proposed
approach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On
all these datasets, our method performs favorably against the state-of-the-arts
in terms of standard image fidelity metrics. In particular, compared to the
best performing method on the MIT-Adobe FiveK dataset, our algorithm improves
PSNR from 23.04 dB to 24.45 dB.
