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Particulate Matter (PM) is a form of air pollution that visually degradesurban scenery and is hazardous to human health and the environment. Currentmonitoring devices are limited in measuring average PM over large areas.Quantifying the visual effects of haze in digital images of urban scenery andcorrelating these effects to PM levels is a vital step in more practicallymonitoring our environment. Current image haze extraction algorithms removehaze from the scene for the sole purpose of enhancing vision. We present twoalgorithms which bridge the gap between image haze extraction and environmentalmonitoring. We provide a means of measuring atmospheric scattering from imagesof urban scenery by incorporating temporal knowledge. In doing so, we alsopresent a method of recovering an accurate depthmap of the scene and recoveringthe scene without the visual effects of haze. We compare our algorithm to threeknown haze removal methods. The algorithms are composed of an optimization overa model of haze formation in images and an optimization using a constraint ofconstant depth over a sequence of images taken over time. These algorithms notonly measure atmospheric scattering, but also recover a more accurate depthmapand dehazed image. The measurements of atmospheric scattering this researchproduces, can be directly correlated to PM levels and therefore pave the way tomonitoring the health of the environment by visual means. Accurate atmosphericsensing from digital images is a challenging and under-researched problem. Thiswork provides an important step towards a more practical and accurate visualmeans of measuring PM from digital images.

Measuring Atmospheric Scattering from Digital Images of Urban Scenery using Temporal Polarization-Based Vision