基于时间偏振视觉的城市景观数字图像大气散射测量
Measuring Atmospheric Scattering from Digital Images of Urban Scenery using Temporal Polarization-Based Vision
摘要：
颗粒物（PM）是一种空气污染形式，它会使城市景观在视觉上退化，并对人类健康和环境造成危害。目前的监测设备在测量大面积的平均PM方面受到限制。量化城市景观数字图像中雾霾的视觉影响，并将这些影响与PM水平相关联，是更实际地监测我们环境的重要一步。目前的图像雾提取算法从场景中去除雾的唯一目的是增强视觉。我们提出了两种算法，弥补了图像雾霾提取和环境监测之间的差距。我们提供了一种测量大气散射从城市风景的图像，结合时间的知识。在这样做的时候，我们还提出了一种方法，恢复一个准确的深度图的场景和恢复的场景没有雾的视觉效果。我们比较我们的算法，三个已知的阴霾去除方法。该算法是由一个优化的图像中的烟雾形成的模型和优化使用的约束，随着时间的推移采取的图像序列的恒定深度。这些算法不仅可以测量大气散射，而且可以恢复更精确的深度图和去雾图像。这项研究产生的大气散射测量结果可以直接与PM水平相关，因此为通过视觉手段监测环境健康铺平了道路。从数字图像中精确地感知大气是一个具有挑战性和研究不足的问题。这项工作提供了一个更实用和准确的视觉手段测量PM从数字图像的重要一步。
英文摘要：
Particulate Matter (PM) is a form of air pollution that visually degrades urban scenery and is hazardous to human health and the environment. Current monitoring devices are limited in measuring average PM over large areas. Quantifying the visual effects of haze in digital images of urban scenery and correlating these effects to PM levels is a vital step in more practically monitoring our environment. Current image haze extraction algorithms remove haze from the scene for the sole purpose of enhancing vision. We present two algorithms which bridge the gap between image haze extraction and environmental monitoring. We provide a means of measuring atmospheric scattering from images of urban scenery by incorporating temporal knowledge. In doing so, we also present a method of recovering an accurate depthmap of the scene and recovering the scene without the visual effects of haze. We compare our algorithm to three known haze removal methods. The algorithms are composed of an optimization over a model of haze formation in images and an optimization using a constraint of constant depth over a sequence of images taken over time. These algorithms not only measure atmospheric scattering, but also recover a more accurate depthmap and dehazed image. The measurements of atmospheric scattering this research produces, can be directly correlated to PM levels and therefore pave the way to monitoring the health of the environment by visual means. Accurate atmospheric sensing from digital images is a challenging and under-researched problem. This work provides an important step towards a more practical and accurate visual means of measuring PM from digital images.
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