Applying a Gaussian blur filter

A blur filter can be useful in many different situations where the goal is to reduce the amount of noise in the image. As mentioned in the previous recipe, applying a blur filter prior to the edge detection pass may improve the results by reducing the amount of high frequency fluctuation across the image. The basic idea of any blur filter is to mix the color of a pixel with that of nearby pixels using a weighted sum. The weights typically decrease with the distance from the pixel (in 2D screen space) so that pixels that are far away contribute less than those closer to the pixel being blurred.

 

A Gaussian blur uses the two-dimensional Gaussian function to weight the contributions of the nearby pixels:

The sigma squared term is the variance of the Gaussian, and determines the width of the Gaussian curve. The Gaussian function is maximum at (0,0), which corresponds to the location of the pixel being blurred and its value decreases as x or y increases. The following...