Vision-Based PM2.5 Concentration Estimation With Natural Scene Statistical Analysis

As the primary pollutant in China's urban atmosphere, particulate matter (PM)
$_{2.5}$
poses a great threat to the health of residents and ecological stability. An efficient and effective PM
$_{2.5}$
concentration monitoring is essential. Nonetheless, the popular devices for PM
$_{2.5}$
monitoring are developed based on two standards: the microoscillation balance method and the
$\beta$
-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM
$_{2.5}$
concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the “haze” formed by PM
$_{2.5}$
. Moreover, we develop a lightweight deep belief network-deep neural network-based PM
$_{2.5}$
concentration estimation model, which learns the mapping from the designed visual priors to PM
$_{2.5}$
concentrations. Experiments confirm the superiority of our vision-based PM
$_{2.5}$
concentration estimation method by comparison with state-of-the-art photo-based PM
$_{2.5}$
monitoring methods.