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→Introduction : GPU Benchmarking/Gaussian Blur Filter : Colin Paul
If you apply multiple blurs, the equivalent blur is the square root of the sum of the squares of the blur. Taking wikipedia’s [https://en.wikipedia.org/wiki/Gaussian_blur example], if you applied a blur with radius 6 and a blur<br/>
with a radius of 8, you’d end up with the equivelant of a radius 10 blur. This is because √ ( 6<sup>2</sup> + 8<sup>2</sup> ) = 10
[[Image:Kernalweightperpixel.PNG|500px|thumb|alt=2D Gaussian]]
There are a couple ways to calculate a Gaussian kernel.
Where the sigma is your blur amount and x ranges across your values from the negative to the positive. For instance, if your kernel was 5 values, it would range from -2 to +2.
An even better way would be to integrate the Gaussian function instead of just taking point samples. Refer to the diagram two graphs on the right.<br/>The diagram plots graphs plot the continuous distribution function and the discrete kernel approximation. One thing to look out for are the tails of the distribution vs. kernel support:<br/>
For the current configuration, we have 13.36% of the curve’s area outside the discrete kernel. Note that the weights are renormalized such that the sum of all weights is one. Or in other words:<br/>
the probability mass outside the discrete kernel is redistributed evenly to all pixels within the kernel. The weights are calculated by numerical integration of the continuous gaussian distribution<br/>
