Two-Stream Non-Uniform Concentration Reasoning Network for Single Image Air Pollution Estimation

Abstract

With the increasing availability of portable cameras and smart phones, directly estimating PM2.5 based on digital photography shows advantages in efficiency and economic costs. In this paper, a novel Two-stream Non-uniform Concentration Reasoning Network (TNCR-Net) is proposed for single image PM2.5 concentration estimation. Motivated by locally non-uniform particle pollution concentration distribution in images, we adopt patch-based scheme and adaptive weighted average mechanism to obtain patch-wise concentration and relative weight based on spatially varying perceptual relevance of local particle pollution concentration. Then aggregate patch-wise concentrations according to relative weights. To learn more effective feature from particular pollution image, we use a two-stream network structure with the dark channel map as the input of one stream. Besides, we employ attention-based feature fusion method to flexibly aggregate the feature maps of the two streams. Experiments on real-world dataset indicate that our TNCR-Net outperforms other state-of-the-art methods with fewer parameters.