  Image enhancement is a common technique used to mitigate issues such as
severe noise, low brightness, low contrast, and color deviation in low-light
images. However, providing an optimal high-light image as a reference for
low-light image enhancement tasks is impossible, which makes the learning
process more difficult than other image processing tasks. As a result, although
several low-light image enhancement methods have been proposed, most of them
are either too complex or insufficient in addressing all the issues in
low-light images. In this paper, to make the learning easier in low-light image
enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two
relative loss functions. Specifically, we first recognize the challenges of the
need for a large receptive field to obtain global contrast and the lack of an
absolute reference, which limits the simplification of network structures in
this task. Then, we propose an efficient global feature information extraction
component and two loss functions based on relative information to overcome
these challenges. Finally, we conducted comparative experiments to demonstrate
the effectiveness of the proposed method, and the results confirm that the
proposed method can significantly reduce the complexity of supervised low-light
image enhancement networks while improving processing effect. The code is
available at \url{https://github.com/hitzhangyu/FLW-Net}.
