Estimation of Particulate Levels Using Deep Dehazing Network and Temporal Prior

Abstract EN

Particulate matters (PM) have become one of the important pollutants that deteriorate public health.

Since PM is ubiquitous in the atmosphere, it is closely related to life quality in many different ways.

Thus, a system to accurately monitor PM in diverse environments is imperative.

Previous studies using digital images have relied on individual atmospheric images, not benefiting from both spatial and temporal effects of image sequences.

This weakness led to undermining predictive power.

To address this drawback, we propose a predictive model using the deep dehazing cascaded CNN and temporal priors.

The temporal prior accommodates instantaneous visual moves and estimates PM concentration from residuals between the original and dehazed images.

The present method also provides, as by-product, high-quality dehazed image sequences superior to the nontemporal methods.

The improvements are supported by various experiments under a range of simulation scenarios and assessments using standard metrics.