Low-light is an inescapable element of our daily surroundings that greatly
affects the efficiency of our vision. Research works on low-light has seen a
steady growth, particularly in the field of image enhancement, but there is
still a lack of a go-to database as benchmark. Besides, research fields that
may assist us in low-light environments, such as object detection, has glossed
over this aspect even though breakthroughs-after-breakthroughs had been
achieved in recent years, most noticeably from the lack of low-light data (less
than 2% of the total images) in successful public benchmark dataset such as
PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark
dataset to elevate this data drought, consisting exclusively of ten different
types of low-light images (i.e. low, ambient, object, single, weak, strong,
screen, window, shadow and twilight) captured in visible light only with image
and object level annotations. Moreover, we share insightful findings in regards
to the effects of low-light on the object detection task by analyzing
visualizations of both hand-crafted and learned features. Most importantly, we
found that the effects of low-light reaches far deeper into the features than
can be solved by simple "illumination invariance'". It is our hope that this
analysis and the Exclusively Dark dataset can encourage the growth in low-light
domain researches on different fields. The Exclusively Dark dataset with its
annotation is available at
this https URL

本文提供了一个独特的 low-light 图像数据集 Exclusively Dark dataset 及其注释，并分析了低光照度对物体检测任务的影响，发现其影响深入特征中，不能仅靠 “光照不变性” 解决。作者希望这个数据集及分析能够鼓励不同领域的低光照度研究的发展。

使用新版独家暗黑数据集熟悉低光照图像

Getting to Know Low-light Images with The Exclusively Dark Dataset

Visual perception is a challenging problem in part due to illumination
variations. A possible solution is to first estimate an illumination invariant
representation before using it for recognition. The object albedo and surface
normals are examples of such representations. In this paper, we introduce a
multilayer generative model where the latent variables include the albedo,
surface normals, and the light source. Combining Deep Belief Nets with the
Lambertian reflectance assumption, our model can learn good priors over the
albedo from 2D images. Illumination variations can be explained by changing
only the lighting latent variable in our model. By transferring learned
knowledge from similar objects, albedo and surface normals estimation from a
single image is possible in our model. Experiments demonstrate that our model
is able to generalize as well as improve over standard baselines in one-shot
face recognition.

本文提出一种多层生成模型，通过将深度置信网与朗伯反射假设相结合，从 2D 图像中学习良好的基于 albedo 的先验。通过改变光源潜变量，可以解释照明变化，并且可以从类似对象中转移学习，从单个图像中估计 albedo 和表面法矢量。实验证明，该模型在一次人脸识别中具有广泛的适应性和改进。