Applies a Gaussian blur effect on a static noise.

The principle of the Gaussian blur is to compute a new value for each pixel of an image, using the average value of the surrounding pixels weighted according to a Gaussian distribution. It means that the closer the surrounding pixels are from the processed pixel, the higher is their weight in the computation of the new pixel value. The window of surrounding pixels, used to compute the mean value, is named the kernel. The kernel size is the size of this window.

Gaussian blur gives better results than the box blur, but it is slightly more computationally expensive.

#### Node inputs/outputs

The post-process parameter is:

- Kernel size: the size of the window of values, used for the blur effects. The maximum number of pixels used to compute the mean value is the square value of kernel size.
- Sigma: the standard-deviation of the Gaussian distribution (a higher value will give more weight to further pixels, and a lower value will reduce their weight).
- Module: the static module that will be processed

The output value is a Static Module.

#### Examples

Gaussian Blur applied on a rasterized Checkerboard