Natural SciencesComputer ScienceVision ScienceAtmospheric ScienceComputer VisionVisual PerceptionAtmospheric EffectsAtmospheric OpticsAtmospheric SeeingImage FormationScene ReconstructionAtmospheric HazeHaze ModelingOutdoor Scene ReconstructionVisibility Under Adverse WeatherVisual Information Systems
Vision systems are built for clear weather, yet outdoor use inevitably encounters bad weather that alters visual information through atmospheric modulation. The study aims to enable computer vision to operate in haze, fog, rain, hail, and snow by examining visual manifestations and exploiting weather‑induced effects. The authors analyze weather visual manifestations using atmospheric optics and develop models to recover scene properties such as 3D structure from images taken in poor weather. The resulting models successfully recover scene properties, including 3D structure, from images captured in adverse weather.
Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from "bad" weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics. Next, we identify effects caused by bad weather that can be turned to our advantage. Since the atmosphere modulates the information carried from a scene point to the observer it can be viewed as a mechanism of visual information coding. Based on this observation, we develop models and methods for recovering pertinent scene properties, such as three-dimensional structure, from images taken under poor weather conditions.
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