如何确定高斯滤波器的窗口大小 [英] How to determine the window size of a Gaussian filter

问题描述

高斯平滑是一种常见的图像处理功能，对于高斯滤波的介绍，请参考此处。我们可以看到，一个参数：标准推导将决定高斯函数的形状。然而，当我们用高斯滤波执行卷积时，另一个参数：高斯滤波器的窗口大小也应该同时确定。例如，当我们使用MATLAB提供的 fspecial 函数时，不仅必须提供标准派生，还要提供窗口大小。直观地，高斯标准推导越大，高斯核窗口应该越大。但是，没有关于如何设置正确窗口大小的一般规则。有任何想法吗？谢谢！

Gaussian smoothing is a common image processing function, and for an introduction of Gaussian filtering, please refer to here. As we can see, one parameter: standard derivation will determine the shape of Gaussian function. However, when we perform convolution with Gaussian filtering, another parameter: the window size of Gaussian filter should also be determined at the same time. For example, when we use fspecial function provided by MATLAB, not only the standard derivation but also the window size must be provided. Intuitively, the larger the Gaussian standard derivation is the bigger the Gaussian kernel window should. However, there is no general rule about how to set the right window size. Any ideas? Thanks!

推荐答案

掩码的 size 驱动过滤器数量。较大的大小，对应于较大的卷积掩码，通常会导致更大程度的过滤。作为更大量降噪的一种权衡，更大的滤波器也会影响图像的细节质量。

The size of the mask drives the filter amount. A larger size, corresponding to a larger convolution mask, will generally result in a greater degree of filtering. As a kinda trade-off for greater amounts of noise reduction, larger filters also affect the details quality of the image.

这是里程碑。现在来到高斯过滤器，标准差是主要参数。如果您使用2D过滤器，在面具边缘，您可能希望权重接近0 。

That's as milestone. Now coming to the Gaussian filter, the standard deviation is the main parameter. If you use a 2D filter, at the edge of the mask you will probably desire the weights to approximate 0.

在这方面，我已经说过了，您可以选择尺寸通常 三次 标准差的面具。这样，几乎整个高斯钟被考虑在内，并且在蒙版的边缘，你的权重将渐近趋于零。

To this respect, as I already said, you can choose a mask with a size which is generally three times the standard deviation. This way, almost the whole Gaussian bell is taken into account and at the mask's edges your weights will asymptotically tend to zero.

我希望这会有所帮助。

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