  Low-light image enhancement, such as recovering color and texture details
from low-light images, is a complex and vital task. For automated driving,
low-light scenarios will have serious implications for vision-based
applications. To address this problem, we propose a real-time unsupervised
generative adversarial network (GAN) containing multiple discriminators, i.e. a
multi-scale discriminator, a texture discriminator, and a color discriminator.
These distinct discriminators allow the evaluation of images from different
perspectives. Further, considering that different channel features contain
different information and the illumination is uneven in the image, we propose a
feature fusion attention module. This module combines channel attention with
pixel attention mechanisms to extract image features. Additionally, to reduce
training time, we adopt a shared encoder for the generator and the
discriminator. This makes the structure of the model more compact and the
training more stable. Experiments indicate that our method is superior to the
state-of-the-art methods in qualitative and quantitative evaluations, and
significant improvements are achieved for both autopilot positioning and
detection results.
