Image-Based PM2.5 Estimation and its Application on Depth Estimation

Air pollution is still a big threat to human health particularly for developing countries. It is highly demanding to measure air quality with daily-used devices such as smartphones. On the other hand, it is difficult to estimate the scene depth under the foul weather using traditional vision-based methods. This paper proposes an image-based method for PM2.5 estimation by capturing a single image. We extract high-level features based on convolutional neural network (CNN) and learn the mapping between the features and PM2.5 by support vector regression (SVR). Given a captured image, we can estimate the PM2.5 value in real time. With the estimated PM2.5, we can estimate the depth of scene using sparse prior and nonlocal bilateral kernel. Experimental results demonstrate that the proposed method achieves the same accuracy of PM2.5 estimation as commodity measurement devices, and estimates the accurate depth information that is even better than the “ground-truth” captured by a laser in the no-haze condition.