Gaussian Blur - Standard Deviation, Radius and Kernel Size

1. What kernel size to use for Gaussian blur?
2. How do I choose kernel size for Gaussian filter?
3. What is the standard deviation of a Gaussian kernel?
4. What is standard deviation in Gaussian blur?

What kernel size to use for Gaussian blur?

Typically, however, it's uncommon to use a kernel size larger than around 50 or so as things are usually already pretty blurry by that point. The Gaussian blur is a great example of simple mathematics put to a powerful use in image processing.

How do I choose kernel size for Gaussian filter?

For example if sigma = 1 then the gaussian is greater than epsilon = 0.01 when x <= 2.715 so a filter radius = 3 (width = 2*3 + 1 = 7) is sufficient. If you reduce/increase epsilon then you will need a larger/smaller radius.

What is the standard deviation of a Gaussian kernel?

The standard deviation for a two-dimensional kernel is the radius in pixels containing 68% of the integrated magnitude of the coefficients.

What is standard deviation in Gaussian blur?

The standard deviation of the Gaussian function controls the amount of blurring. A large standard deviation (i.e., > 2) significantly blurs, while a small standard deviation (i.e., 0.5) blurs less. If the objective is to achieve noise reduction, a rank filter (median) might be more useful in some circumstances.