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

机译:一种新的用于大气点扩展功能的卷积核应用于计算机视觉

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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,我们介绍了一种新的滤镜,可以近似估计参与介质内光线的多次散射。该滤波器是从广义高斯分布GGD导出的。它表征了大气点扩散函数(APSF),因此可以引入三种新方法。首先,它使我们能够精确地模拟各种天气条件,这些条件会引起多种散射,包括雾,霾,雨等。第二,它使我们能够提出一种新的方法来协同并同时估计视觉线索,即识别天气在未知天气条件下获取的同一场景的两幅图像之间的光学退化和光学厚度估计。第三,通过将该滤波器与我们最近开发的两组新的不变特征组合,我们获得了可用于匹配大气退化图像的不变特征。第一组导致大气不变性,而第二组同时提供大气和几何不变性。

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