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.

中文翻译：

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探索低光图像中显着目标检测的图像增强

在非均匀照明环境中捕获的弱光图像通常会随着场景深度和相应的环境光而退化。这种退化导致退化图像模态中严重的对象信息丢失，由于低对比度特性和人造光的影响，这使得显着目标检测更具挑战性。然而，现有的显着目标检测模型是基于图像在足够亮度环境下捕获的假设而开发的，这在现实世界场景中是不切实际的。在这项工作中，我们提出了一种图像增强方法，以促进低光图像中的显着目标检测。所提出的模型直接将物理光照模型嵌入到深度神经网络中来描述低光图像的退化，其中环境光被视为逐点变量并随本地内容而变化。此外，利用非局部块层来捕获对象的局部内容与其局部邻域偏爱区域的差异。为了进行定量评估，我们构建了一个带有像素级人工标记的真实标注的低光图像数据集，并在四个公共数据集和我们的基准数据集上报告了有希望的结果。