Low light images captured in a non-uniform illumination environment usually
are degraded with the scene depth and the corresponding environment lights.
This degradation results in severe object information loss in the degraded
image modality, which makes the salient object detection more challenging due
to low contrast property and artificial light influence. However, existing
salient object detection models are developed based on the assumption that the
images are captured under a sufficient brightness environment, which is
impractical in real-world scenarios. In this work, we propose an image
enhancement approach to facilitate the salient object detection in low light
images. The proposed model directly embeds the physical lighting model into the
deep neural network to describe the degradation of low light images, in which
the environment light is treated as a point-wise variate and changes with local
content. Moreover, a Non-Local-Block Layer is utilized to capture the
difference of local content of an object against its local neighborhood
favoring regions. To quantitative evaluation, we construct a low light Images
dataset with pixel-level human-labeled ground-truth annotations and report
promising results on four public datasets and our benchmark dataset.

本文提出了一种基于物理光照模型的图像增强方法，以便在低光照图像中便于显着物体检测，并使用 Non-Local-Block 层来捕捉物体与其邻域的差异，最终在 4 个公共数据集和自己构建的数据集中取得了有前景的结果。