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含噪图像中真实灰度级数量、取值及相对频率的估计AI 解读

In some applications information is presented as a two-dimensional image corrupted by random noise. Due to the precision of the equipment that forms the image, we can typically have a large number, v, of observed gray levels. But in many situations we know that the number of true gray levels, p, corresponding to, for example, the number of tissue types in a brain slice, is much less than v. In this article we propose a method based on the kernel density estimator for…

噪声图像中真实灰度级数量、取值与相对频率的估计AI 解读

In some applications information is presented as a two-dimensional image corrupted by random noise. Due to the precision of the equipment that forms the image, we can typically have a large number, v, of observed gray levels. But in many situations we know that the number of true gray levels, p, corresponding to, for example, the number of tissue types in a brain slice, is much less than v. In this article we propose a method based on the kernel density estimator for…