How to determine the window size of a Gaussian filter

Question

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

Answer 1

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.

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.

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.

I hope this helps.