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A multidimensional signal can be represented in terms of sinusoidal components. This is typically done with a type of [[Fourier transform]]. The M-D [[Fourier transform]] transforms a signal from a [[time ___domain]] representation to a [[frequency ___domain]] representation of the signal. In the case of digital processing, a [[discrete time Fourier transform]] is utilized to transform a sampled time ___domain representation into a frequency ___domain representation:
:<math> X(k_1,k_2,\dots,k_m) = \sum_{n_1=-\infty}^\infty \sum_{n_2=-\infty}^\infty \cdots \sum_{n_m=-\infty}^\infty x(n_1,n_2,\dots,n_m) e^{-j k_1 n_1} e^{-j k_2 n_2} \cdots e^{-j k_m n_m}</math>
where ''X'' stands for the multidimensional discrete Fourier transform, ''x'' stands for the sampled time ___domain signal, ''m'' stands for the number of dimensions in the system, ''n'' are time samples and ''k'' are frequency samples<ref name="dudmer83_2">D. Dudgeon and R. Mersereau, Multidimensional Digital Signal Processing, Prentice-Hall, First Edition, pp. 61,112, 1983.</ref>.
Computational complexity is usually the main concern when implementing any Fourier transform. For multidimensional signals, the complexity can be reduced by a number of different methods. The computation may be simplified if there is [[independence]] between [[variables]] of the multidimensional signal<ref name="dudmer83_2"/>. In general, [[Fast Fourier Transforms]] (FFTs), utilize efficiencies of the system to reduce the number of computations by a substantial factor. While there are a number of different implementations of this [[algorithm]] for m-D signals, two often used variations are the vector-radix FFT and the row-column FFT.
== Filtering ==
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