Haze Density Estimation via Modeling of Scattering Coefficients of Iso-depth Regions
This work addresses the need for precaution alarms and emergency reactions during hazy weather, providing a vision-based solution for haze density estimation, though it appears incremental as it builds on existing methods like superpixel segmentation.
The paper tackles the problem of estimating haze density from images by introducing a framework based on modeling scattering coefficients of iso-depth regions, proposing the Normalized Scattering Coefficient (NSC) metric to measure haze levels with reference to two scales, and achieving reliable predictions for outdoor scenarios using superpixel segmentation and a robust dark SP selection method.
Vision based haze density estimation is of practical implications for the purpose of precaution alarm and emergency reactions toward disastrous hazy weathers. In this paper, we introduce a haze density estimation framework based on modeling of scattering coefficients of iso-depth regions. A haze density metric of Normalized Scattering Coefficient (NSC) is proposed to measure current haze density level with reference to two reference scales. Iso-depth regions are determined via superpixel segmentation. Efficient searching and matching of iso-depth units could be carried out for measurements via unstationary cameras. A robust dark SP selection method is used to produce reliable predictions for most out-door scenarios.