Can I smooth 1D data taken from pages of 3D array without using for loops?

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Ben C
Ben C 2014-2-4
评论： Ben C 2014-2-5
I am looking to take a 3D array (~1000x1000x18) and take the vector along each page at each x,y point [e.g. (1,1,:); (1,2,:) etc.] and smooth or fit it to find the max value at each point. Each of the vectors are noisy cosine squared and I want to be able to get a true max at each value. Right now the noise is such that max(X,[],3) gives a different result than I want. So I want to smooth or fit each vector of data to get a better max of each vector out.
I can think of a brute force way to do that using for loops to pull each vector out one at a time but was wondering if this a nicer way.

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Matt J
Matt J 2014-2-4
You could apply a 1D convolution along the 3rd dimension as follows,
kernel=reshape(kernel,1,1,[]);
convn(yourarray,kernel,'same')
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Matt J
Matt J 2014-2-4
FFT low pass filtering processing might be even better, since your square cosine is highly bandlimited
F=fft(yourarray,[],3).*lowpass_mask;
f=ifft(F,[],3);
result = max(f,[],3);

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