Particulate matters (PM) have become one of the important pollutants that deteriorate public health. Since PM is ubiquitous in the atmosphere, it is closely related to life quality in many different ways. Thus, a system to accurately monitor PM in diverse environments is imperative. Previous studies using digital images have relied on individual atmospheric images, not benefiting from both spatial and temporal effects of image sequences. This weakness led to undermining predictive power. To address this drawback, we propose a predictive model using the deep dehazing cascaded CNN and temporal priors. The temporal prior accommodates instantaneous visual moves and estimates PM concentration from residuals between the original and dehazed images. The present method also provides, as by-product, high-quality dehazed image sequences superior to the nontemporal methods. The improvements are supported by various experiments under a range of simulation scenarios and assessments using standard metrics.

中文翻译：

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使用深度除雾网络和时间先验估计颗粒物水平

颗粒物（PM）已成为使公众健康恶化的重要污染物之一。由于PM在大气中无处不在，因此它以许多不同的方式与生活质量密切相关。因此，在各种环境中准确监视PM的系统势在必行。以前使用数字图像进行的研究都依赖于单个大气图像，无法从图像序列的时空效应中受益。这种弱点导致破坏了预测能力。为了解决这个缺点，我们提出了使用深度除雾级联的CNN和时间先验的预测模型。时间先验适应瞬时视觉移动，并根据原始图像和模糊图像之间的残差估算PM浓度。本方法还提供了作为副产物的 优于非时域方法的高质量除雾图像序列。在一系列模拟方案和使用标准指标进行的评估下，各种实验均支持了这些改进。