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.

1. What is the standard deviation of a Gaussian kernel?
2. What is sigmaX and sigmaY in Gaussian blur?
3. How is Gaussian blur calculated?
4. How does sigma affect Gaussian blur?

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 sigmaX and sigmaY in Gaussian blur?

sigmax - standard deviation in X direction; if 0, calculated from kernel size. sigmay - standard deviation in Y direction; if sigmaY is None, sigmaY is taken to equal sigmaX.

How is Gaussian blur calculated?

To put it simply, the Gaussian blur algorithm is a process of performing a weighted average operation on the entire image. The value of each pixel is obtained by weighted averaging of itself and other pixel values in the field.

How does sigma affect Gaussian blur?

The value of σ controls the variance around a mean value of the Gaussian distribution, which determines the extent of the blurring effect around a pixel.