PM2.5 does great harm to human beings. In particular, it can lead to an increase in human lung cancer. In this paper, we propose a PM2.5 concentration estimator based on deep convolutional neural networks. The proposed method consists of three modules. First, we generate a hallucinated reference image of PM2.5 by using deep convolutional neural networks. The discrepancy map of the PM2.5 image and the hallucinated reference image are calculated. Second, the discrepancy map and the distorted PM2.5 image are used to extract the features. Third, the prediction module based on neural networks utilizes those extracted features to predict PM2.5 concentrations. Compared to existing PM2.5 estimators and state-of-art image quality assessment(IQA) metrics, experimental results illustrate the effectiveness of the proposed model on the AQID database.

PM2.5对人体危害很大。特别是，它可以导致人类​​肺癌的增加。在本文中，我们提出了一种基于深度卷积神经网络的 PM2.5 浓度估计器。所提出的方法由三个模块组成。首先，我们使用深度卷积神经网络生成 PM2.5 的幻觉参考图像。计算 PM2.5 图像和幻觉参考图像的差异图。其次，使用差异图和扭曲的 PM2.5 图像提取特征。第三，基于神经网络的预测模块利用提取的特征来预测 PM2.5 浓度。与现有的 PM2.5 估计器和最先进的图像质量评估 (IQA) 指标相比，实验结果说明了所提出的模型在 AQID 数据库上的有效性。