Gaussian Blur Processing

Gaussian Blur

Removing Noise in Image By applying a slight blur, we can remove the highest-frequency information (a.k.a noise) from the image, which will give us “smoother” blocks of color that we can analyze.
A blur just takes more averages of pixels.This averaging process is a type of kernel convolution
Steps to generate a blur:

1.Select a pixel in the photo and determine it’s value
2.Find the values for the selected pixel’s local neighbors (we can arbitrarily define the size of this “local region”, but it’s typically fairly small)
3.Take the value of the original pixel and the neighbor pixels and average them together using some weighting system
4.Replace the value of the original pixel with the outputted averaged value
5.Do this for all pixels

So “make all the pixels more similar to the pixels nearby”, which intuitively sounds like blurring
For a Gaussian Blur, we are simply using the Gaussian Distribution (i.e. a bell curve) to determine the weights in Step 3 above. This means that the closer a pixel is to the selected pixel, the greater its weight.