Discern Depth Under Foul Weather: Estimate PM$_{2.5}$ for Depth Inference

Abstract

Nowadays, haze is a common and serious problem and PM $_{2.5}$ is a main measurement for air quality. Current methods estimate the level of primary pollutant with professional instruments, which is expensive and inconvenient. Moreover, with haze, the captured images will be unclear and are difficult to estimate the depth of the scene using passive methods. This article proposes a cheap, fast, and convenient PM $_{2.5}$ estimation method that only need a captured image using daily-life devices, and further, discerns the depth of the scene using the estimated PM $_{2.5}$ . We learn haze-relevant classified mapping via the hybrid convolutional neural network and combine the high-level features extracted from the convolutional layer with ground-truth PM $_{2.5}$ to train support vector regression. The transmission map is computed using nonlocal sparse priors, and the depth map is inferred using the estimated PM $_{2.5}$ value through the atmospheric scattering model. Experimental results demonstrate that our method achieves accurate PM $_{2.5}$ estimation and depth inference. This could be very useful in many applications, for both clean and foul weather.