Deep Quantified Visibility Estimation for Traffic Image

Abstract

Image-based quantified visibility estimation is an important task for both atmospheric science and computer vision. Traditional methods rely largely on meteorological observation or manual camera calibration, which restricts its performance and generality. In this paper, we propose a new end-to-end pipeline for single image-based quantified visibility estimation by an elaborate integration between meteorological physical constraint and deep learning architecture design. Specifically, the proposed Deep Quantified Visibility Estimation Network (abbreviated as DQVENet) consists of three modules, i.e., the Transmission Estimation Module (TEM), the Depth Estimation Module (DEM), and the Extinction coEfficient Estimation Module (E3M). Casting on these modules, the meteorological prior constraint can be combined with deep learning. To validate the performance of DQVENet, this paper also constructs a traffic image dataset (named QVEData) with accurate visibility calibration. Experimental results compared with many state-of-the-art methods on QVEData demonstrate the effectiveness and superiority of DQVENet.

MAFormer: A New Method for Radar Reflectivity Reconstructing Using Satellite Data

“…Convolutional neural networks (CNNs) have been widely employed in weather forecasting tasks, primarily for the analysis of meteorological images and satellite data. CNNs excel at capturing spatial dependencies in data, making them suitable for tasks such as meteorological forecasting [1], spatial downscaling [2,3], weather classification [4,5], and cloud classification [6]. Han et al [7] transform meteorological nowcasting into two stages, i.e., precipitation level classification and accurate precipitation regression.…”