  Real-time transportation surveillance is an essential part of the intelligent
transportation system (ITS). However, images captured under low-light
conditions often suffer the poor visibility with types of degradation, such as
noise interference and vague edge features, etc. With the development of
imaging devices, the quality of the visual surveillance data is continually
increasing, like 2K and 4K, which has more strict requirements on the
efficiency of image processing. To satisfy the requirements on both enhancement
quality and computational speed, this paper proposes a double domain guided
real-time low-light image enhancement network (DDNet) for ultra-high-definition
(UHD) transportation surveillance. Specifically, we design an encoder-decoder
structure as the main architecture of the learning network. In particular, the
enhancement processing is divided into two subtasks (i.e., color enhancement
and gradient enhancement) via the proposed coarse enhancement module (CEM) and
LoG-based gradient enhancement module (GEM), which are embedded in the
encoder-decoder structure. It enables the network to enhance the color and edge
features simultaneously. Through the decomposition and reconstruction on both
color and gradient domains, our DDNet can restore the detailed feature
information concealed by the darkness with better visual quality and
efficiency. The evaluation experiments on standard and transportation-related
datasets demonstrate that our DDNet provides superior enhancement quality and
efficiency compared with the state-of-the-art methods. Besides, the object
detection and scene segmentation experiments indicate the practical benefits
for higher-level image analysis under low-light environments in ITS.
