How to determine the window size of a Gaussian filter
image-processing, matlab, signal-processing
Solution
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.
Problem
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!