Low Pass image filter

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I am interested in learning how to program a low-pass image filter like the one in photoshop. Can anyone recommend some good tutorials on how to do this?

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Gaussian blur is a more common name to a low-pass image filter.
It is discussed in this thread, among others.

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I mean High-Pass. Do I need to do FFTs?

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Not necessarily. You could subtract the blurred image from the original if you need a high-shelf pass.

FFT does give more flexibility to band-pass algorithms, though.

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i dont need band pass. I just need a high pass, faster the better.

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Take the image, do the low-pass (gaussian blur), subtract the blurred version from the original version. Bingo: high pass.

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Thanks for that advise. Is that the fastest way?

oh and what do you mean by subtract? EG if the original version has a black pixel and the corresponding pixel on the blurred version is dark gray?

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Laplacian mask is the standard "high pass" filter.

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If you choose to use the blur-subtract method, be sure to do so in signed number space, and normalize the result to 0...1 space after the operation, since the result may well be negative. Photoshop's high pass filter, for example, normalizes so that 50% gray is the "empty" band color by adding [0.5, 0.5, 0.5] (but this could be controlled very easily with another bias factor during normalizing).

Laplacian matrix is indeed equivalent to the blur-subtract method described here, and is actually faster since the core operation needs only one step (and the bias as needed).

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Ok, this "LaPlacian Mask" sounds like the way to go... Can anyone suggest any good URLs with tuts for this?

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Quote:
 Original post by NodgerOk, this "LaPlacian Mask" sounds like the way to go... Can anyone suggest any good URLs with tuts for this?

it's convoluting with kernel
 0  1  0 1 -4  1 0  1  0`

Or similar kernel(maybe with some values instead of 0's, maybe multiplied by some constant, etc). Basically a Gaussian blur kernel but with center equal to -(summ of other cells).

Note that if you're working on pixels storen in one byte per color component, you might need to add 127 , you'll have negative results.

http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm

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