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A New Convolution Kernel for Atmospheric Point Spread Function Applied to Computer Vision

机译:用于电脑视觉的大气点传播功能的新卷积核

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In this paper we introduce a new filter to approximate multiple scattering of light rays within a participating media. This filter is derived from the generalized Gaussian distribution GGD. It characterizes the Atmospheric Point Spread Function (APSF) and thus makes it possible to introduce three new approaches. First, it allows us to accurately simulate various weather conditions that induce multiple scattering including fog, haze, rain, etc. Second, it allows us to propose a new method for a cooperative and simultaneous estimation of visual cues, i.e., the identification of weather degradations and the estimation of optical thickness between two images of the same scene acquired under unknown weather conditions. Third, by combining this filter with two new sets of invariant features we recently developed, we obtain invariant features that can be used for the matching of atmospheric degraded images. The first set leads to atmospheric invariant features while the second one simultaneously provides atmospheric and geometric invariance.
机译:在本文中,我们引入了一种新的滤波器,以近似参与媒体内的光线散射。该过滤器来自广义高斯分布GGD。它表征了大气点传播功能(APSF),从而使得可以引入三种新方法。首先,我们可以准确地模拟诱导多个散射的各种天气条件,包括雾,雾度,雨等。第二,它允许我们提出一种新的方法,用于协同和同时估计视觉提示,即天气识别在未知天气条件下获取的同一场景的两个图像之间的降解和光学厚度的估计。第三,通过将此过滤器与我们最近开发的两套新的不变功能相结合,我们获得了不变的功能,可以用于大气降级图像的匹配。第一组导致大气不变特征,而第二个同时提供大气和几何不变性。

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