FFT-based convolution

Discrete convolution using FFT method
Updated 14 Jun 2021

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As opposed to Matlab CONV, CONV2, and CONVN implemented as straight forward sliding sums, CONVNFFT uses Fourier transform (FT) convolution theorem, i.e. FT of the convolution is equal to the product of the FTs of the input functions.
In 1-D, the complexity is O((na+nb)*log(na+nb)), where na/nb are respectively the lengths of A and B.
Optional arguments to control the dimension(s) along which convolution is carried out.
Slightly less accurate than sliding sum convolution.
Good usage recommendation:
In 1D, this function is faster than CONV for nA, nB > 1000.
In 2D, this function is faster than CONV2 for nA, nB > 20.
In 3D, this function is faster than CONVN for nA, nB > 5.

Cite As

Bruno Luong (2024). FFT-based convolution (https://www.mathworks.com/matlabcentral/fileexchange/24504-fft-based-convolution), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

Fix bug when calling with syntax CONV2FFT(H1, H2, A, SHAPE)

Installation script use now right mex compilation options for R2018a or later

Make inplaceprod compatible with interleaved complex

Add the syntax conv2fft(H1, H2, A, ...)

Option allows to disable padding to next power-two. Mex implement inplace product that saves about 1/3 memory. These two enhancement might be useful when perform convolution with very large arrays.

GPU unable by default + changes in help section

GPU/Jacket capable

correct bug when ndims(A)<ndims(B)