gausian blur kernel calculation

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Hi does somebody know a website/applet to produce different gausian blur kernels? I want to setup the parameters for example a 3x3, 5x5, 9x9 Kernel (or others) and see the weights of a generated kernel example. Does anybody know of a good example for this? thank you

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If you check out the Gaussian blur page at Wikipedia, it has the equation you would use to generate the values of the kernel. Unless there's any specific reason you need a website/applet that does it, because I'm not aware of any off the top of my head. But you could try Googling for one.

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Thanks. I've read the article but was asking cause I havn't understood everything. :)

I want to blur a texture and tested a 3x3 blur kernel for that (cause Matrix4x4 is my largest possible type).

I've not really understood the meaning of the "standard deviation" sigma and how does it affect/represent the latter kernel?

Until I know what to do with it I used sigma=1.0. But the sum of the weights created were not 1.0 - so if calculated with a set of texel the brightness change. Is it the correct way to go to scale the kernel to the sum of 1.0 (every element * 1/sum) or would a different kernel be correct (using a specific sigma)?

But with that kernel I blur a certain texel with 100%. But what if I just want to blur a texel by for example 30%. Would it simply go like this
new_texel = (original_texel * 0.7) + (blurred_texel * 0.3) or would these adjustments also be done to the kernel?

I was thinking about maybe using a 9x9 Matrix with 9 Matrix3x3 - or is it usually not worth the effort (on a GPU)?

This is BTW my 3x3 kernel:
0,075114 0,123841 0,0751140,123841 0,204180 0,1238410,075114 0,123841 0,075114
Any help with this would be really great. Thank you!

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Quote:
 Original post by quastyI've not really understood the meaning of the "standard deviation" sigma and how does it affect/represent the latter kernel?

The standard deviation in this case controls how "wide" your distribution is. For example, about 2/3 of the weighting (the area under the curve) will fall within one standard deviation of the mean (middle).

Quote:
 Original post by quastyIs it the correct way to go to scale the kernel to the sum of 1.0 (every element * 1/sum) or would a different kernel be correct (using a specific sigma)?

Since the gaussian function goes on to infinity, you're never going to get exactly one. Thus using a "clamped" gaussian and rescaling your weights so that they sum to one is the usual method.

Quote:
 Original post by quastyBut what if I just want to blur a texel by for example 30%.

I'm not sure what you mean, but to get a blurrier image, you should increase sigma and probably increase the number of samples that you are taking as well.

Quote:
 Original post by quastyI was thinking about maybe using a 9x9 Matrix with 9 Matrix3x3 - or is it usually not worth the effort (on a GPU)?

The great thing about the gaussian function is that it's separable... i.e. you can do it in one axis at a time. Unless you need to do it in a single pass, I'd highly recommend doing a horizontal blur pass followed by a vertical blur pass for efficiency. This will *really* start to make a difference with large blur kernels like 9x9, etc.

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Quote:
 Original post by quastyBut with that kernel I blur a certain texel with 100%. But what if I just want to blur a texel by for example 30%.

It's funny you should ask, because for my senior project I'm working on some terrain generation stuff (among other things) and had to ask myself a similar question. I wanted one of the color channels in a heightmap to encode the "smoothness" of the terrain at that point, so somehow I had to modify the moving average filter I was using to take into account variable degrees of smoothness. What I came up with was a weighted moving average filter variant. You start out with a regular (2N+1)x(2N+1) moving average filter kernel, and then scale all the non-center weights by X/Xmax, where X is the value of your smoothness input and Xmax is the maximum value. Then you reassign the center weight to 1 - (sum of non-center weights) to re-normalize. I don't know if such a technique already exists or has a name, but it produces good results. You might try and see if such a technique works for Gaussian distribution filters (moving average is technically a uniform distribution).

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