How to convolve a 3D matrix along one of its dimension?

Question

Yuxin Tong 2022-4-26

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评论： Yuxin Tong 2022-4-27

I have a 100by100by2000 ish matrix, say 3rd dimention is representing time where the 100*100 matrix changes. I wanted to convolve this whole matrix through a temporal gaussian filter along the 3rd dimention (the one that is 2000ish long).

With the code I know of I can only think of making a for-loop and do conv() 10000 times. (cuz I believe conv() can only operat on single dimention vectors?) However this for-loop will then be extremely long and it seems like it would take forever to run. I have attached a visual illustration of what I wanted to do.

So my question is: is there a function that is equivilent to conv() that can allow me to do this in more eifficient way? Or can I actually make conv() to run in a vectorized way?

I am aware that there is a fucntion called filter(), but I wasnt really sure what does that function do. If filter actually would work can anyone tell me what is the similarity and difference between filter and conv?

Thx a lot.

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Answer 1

Matt J 2022-4-26

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If your Gaussian kernels is 1x1xN, you can just use convn as usual

kernel=reshape(gaussianProfile,1,1,[]);
output=convn(Array, kernel);

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Yuxin Tong 2022-4-27

Thx! I think this worked!

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Answer 2

Akira Agata 2022-4-26

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The function smoothdata must be applicable, like:

% Sample data

A = rand(100, 100, 2000);

% Gaussian filter window

win = 15;

% Apply smoothdata along 3rd dimension

dim = 3;

A2 = smoothdata(A, dim, 'gaussian', win);

% Let's check!

figure

plot(squeeze(A(1, 1, :)),'c')

hold on

plot(squeeze(A2(1, 1, :)),'b')

legend({'Original', 'After filtering'})

title('Signal A(1,1,:)')

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Answer 3

Matt J 2022-4-26

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You can use ffts

kernel=reshape(gaussianProfile,1,1,[]);
N=size(Array,3)+size(kernel,3)-1;
F=@(z)fft(z,N,3);
invF=@(z)ifft(z,[],3,'symmetric');
kernel=reshape(gaussianProfile,1,1,[]);
output = invF( F(kernel).*F(Array) );