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李等人指出, 对于图像的可见性, 暗通道的强度和图像的对比度可作为模糊和清晰图像分类的特征 [36]
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图像视觉对比度的测量方法. et[38] 首次由约布森提出. 利用大气散射模型, 研究了不同雾状图像的角偏差, 并给出了与雾状图像分类相同场景的清晰图像 [39]。他们还利用 SVM 对浓雾图像进行分类。虽然它们的方法可以获得良好的分类性能, 但在实际应用中很难同时获得清晰的图像和相同场景的雾状图像。
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余等提取了图像的可见性、图像的视觉对比度以及暗通道作为特征并且使用(SVM) 对浓雾图像分类的强度 [37]
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Pavlic从傅立叶变换的功率谱和支持向量机在高速公路上的车辆视觉系统中, 提出了一种利用全局特征的多雾图像分类方法
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首先使用了Canny-Deriche 过滤器来提取图像边缘来高亮道路的边缘。然后采用区域生长算法对寻找道路表面层。第三, 他们建立了四条件以获得目标区域。最后, 通过计算测量带宽, 得到了图像的可见距离。
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Ancuti 等人首先提出了一种基于 "半逆" 图像的雾区检测算法. 通过选择原始图像像素的和其逆图像像素最大值, 得到了半逆图像S , 该方法被公式化为
Sc (x) = max [Ic(x),1 − Ic(x)] (1)
其中c表示其中一个 RGB 通道, I是原始图像, 1− Ic (x) 表示原始图像的逆图像.
在renormalizing 反图像后, Ancuti 检测到Lch颜色空间h*通道中的雾区., 并将半逆图像和原始图像之间有较大差异的像素视为清晰像素, 并将剩余像素视为雾像素。这种雾区检测方法的基础是, 图像雾区像素的强度值通常比清晰区域的像素大得多。在图像的天空或雾区中, 像素通常在所有颜色通道中都具有高强度, 即Ifog c (x) > 0.5. 因此, 半逆图像将与这些区域中的原始图像具有相同的值。但是, 在清晰区域, 半逆图像至少有一个通道, 像素值将被逆图像替换。换言之, 这个公式(1)的输出分别是雾区的原始图像和清晰区域的逆图像。然后雾区可以通过到原始图像和它的半逆图像之间的差异很容易地检测。该算法简单有效, 可用于雾状图像中的雾区检测, 但不适合当前场景是否有雾的判断。这是因为天空区域或清晰图像的白色区域将被误认为是通过这个算法的雾区.
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[16] S. Alami, A. Ezzine, and F. Elhassouni, “Local fog detection based on saturation and RGB-correlation,” Proc. IEEE International Conference Computer Graphics, Imaging and Visualization, pp.1–5, March 2016.
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