可分离的2D模糊内核 [英] Separable 2D Blur Kernel

问题描述

我们都知道高斯核的可分离性. 还有其他常见的可分离模糊内核吗?

We all know the separability property of a Gaussian Kernel. Are there any other Separable Blur Kernel which are common?

我正在寻找一个减少速度几乎与高斯模糊一样快的内核.

I'm looking for a kernel which decreases almost as fast as Gaussian Blur.

出于各种原因，我无法使用高斯模糊. 我希望不需要三角函数的东西(否则，我会使用像Hann这样的"Windows").

I can't use Gaussian Blur for various reasons. I would prefer something which doesn't require Trigonometric Functions (Else I would use some kind of "Windows" like Hann).

谢谢.

推荐答案

一个人可以使用一维信号处理中经典的已知窗口:
http://en.wikipedia.org/wiki/Window_function

One could use the known windows which are classic in 1D Signal Processing:
http://en.wikipedia.org/wiki/Window_function

可以使用外部产品创建2d内核.
实现应与任何可分离的过滤器一样.

A 2d Kernel could be created using Outer product.
Implementation should be as in any separable filter.

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