The visual quality of photographs taken under imperfect lightness conditions
can be degenerated by multiple factors, e.g., low lightness, imaging noise,
color distortion and so on. Current low-light image enhancement models focus on
the improvement of low lightness only, or simply deal with all the degeneration
factors as a whole, therefore leading to a sub-optimal performance. In this
paper, we propose to decouple the enhancement model into two sequential stages.
The first stage focuses on improving the scene visibility based on a pixel-wise
non-linear mapping. The second stage focuses on improving the appearance
fidelity by suppressing the rest degeneration factors. The decoupled model
facilitates the enhancement in two aspects. On the one hand, the whole
low-light enhancement can be divided into two easier subtasks. The first one
only aims to enhance the visibility. It also helps to bridge the large
intensity gap between the low-light and normal-light images. In this way, the
second subtask can be shaped as the local appearance adjustment. On the other
hand, since the parameter matrix learned from the first stage is aware of the
lightness distribution and the scene structure, it can be incorporated into the
second stage as the complementary information. In the experiments, our model
demonstrates the state-of-the-art performance in both qualitative and
quantitative comparisons, compared with other low-light image enhancement
models. In addition, the ablation studies also validate the effectiveness of
our model in multiple aspects, such as model structure and loss function. The
trained model is available at
this https URL

本文提出了一种将低光图像增强模型拆分成两个阶段的方法，第一阶段侧重于基于像素级的非线性映射来提高场景的可见性，第二阶段侧重于通过抑制其余退化因素来提高外观保真度，本文的实验结果表明该模型在定性和定量比较中具有最先进的性能。