(cross post of SO: https://stackoverflow.com/questions/27345861/extending-1d-function-across-3-dimensions-for-data-windowing )

For the sake of image (volume) registration, I'd like to apply a windowing function to input data, such that the non-periodic image boundaries do not cause streaking in the FFT. I'm using the example from here for 2D data:


h = scipy.signal.hamming(n)
ham2d = sqrt(outer(h,h))

Is this extensible to 3D or even N-D?


Yes, a window function just applies a weighting function to your data.

For N-D data you can view the window function as a combination of N 1-D windows which are all orthogonal to each other.

As the weights of the 1-D windows to not depend on the other dimensions you can either apply each separately or combine them to get a single N-D window.

e.g. for 3D (in matlab, it should be simple enough to translate to python)

hammx=hamming(L); %1D window
hamz=permute(hamx, [3, 2, 1]);
ham3=ones(L, L, L);
for i=1:100               % there is probably a much better way to compute this
ham2(i, :)=hamx(i).*hamy;
for j=1:100
ham3(i, j, :)=ham2(i, j).*hamz;
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  • $\begingroup$ I have posted my Python code solution on Stack Overflow: stackoverflow.com/questions/27345861/… Thanks for your suggestion. $\endgroup$ – msarahan Dec 21 '14 at 3:02
  • $\begingroup$ I down voted because it only discussed the trivial extension. There are more ways of going from 1d to 2d $\endgroup$ – user28715 Jun 15 '17 at 23:30

this question is old, but I needed same function.

I wrote a function that receive one-dimensional window as parameter and transformed into a cube window

function [win] = window3D(w)

    L = size(w,1);
    m1 = w(:)*w(:).';
    win1 = repmat(m1, [1 1 L]);
    m2 = w(:)*ones(1,L);
    win2 = repmat(m2, [1 1 L]);
    win2 = permute(win2, [3, 2, 1]);
    win = win1.*win2;
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  • $\begingroup$ This answer works wonderfully well $\endgroup$ – gaborous Feb 25 at 22:58

For those who would like a Python version of Angel's answer, here it is:

import numpy as np

def window3D(w):
    # Convert a 1D filtering kernel to 3D
    # eg, window3D(numpy.hanning(5))
    m1=np.outer(np.ravel(w), np.ravel(w))
    return win
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Separable extensions are trivial: you can obtain an n-D window with a product of 1-D windows, and they can be different to account for image or volume anisotropy, etc. Fast to compute (separability), but has drawbacks: low-rank, and sometimes a very fast decay: the decays in each dimension are multiplied.

Non-separable extensions can be interesting. If you have a formula for a 1-D discrete window, you can extend it by changing the 1-D center minus lag $|c-l|$ by a more generic norm/quasi-norm in n-D: $||c(x,y,z)-l(x,y,z)||$. The shape of the norm will drive the isotropy, decay, etc.

And you can start from a continuous formula, and discretize and normalize it properly.

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