Theorem in mathematics
In mathematics, the convolution theorem states that under suitable conditions the Fourier transform of a convolution of two functions (or signals) is the pointwise product of their Fourier transforms. More generally, convolution in one domain (e.g., time domain) equals point-wise multiplication in the other domain (e.g., frequency domain). Other versions of the convolution theorem are applicable to various Fourier-related transforms.
Functions of a continuous variable
Consider two functions and with Fourier transforms and :
where denotes the Fourier transform operator. The transform may be normalized in other ways, in which case constant scaling factors (typically or ) will appear in the convolution theorem below. The convolution of and is defined by:
In this context the asterisk denotes convolution, instead of standard multiplication. The tensor product symbol is sometimes used instead.
The convolution theorem states that:[1][2]: eq.8
| | (Eq.1a) |
Applying the inverse Fourier transform produces the corollary:[2]: eqs.7, 10
Convolution theorem
| | (Eq.1b) |
The theorem also generally applies to multi-dimensional functions.
Multi-dimensional derivation of Eq.1
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Consider functions in Lp-space with Fourier transforms :
where indicates the inner product of : and
The convolution of and is defined by:
Also:
Hence by Fubini's theorem we have that so its Fourier transform is defined by the integral formula:
Note that Hence by the argument above we may apply Fubini's theorem again (i.e. interchange the order of integration):
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This theorem also holds for the Laplace transform, the two-sided Laplace transform and, when suitably modified, for the Mellin transform and Hartley transform (see Mellin inversion theorem). It can be extended to the Fourier transform of abstract harmonic analysis defined over locally compact abelian groups.
Periodic convolution (Fourier series coefficients)
Consider -periodic functions and which can be expressed as periodic summations:
- and
In practice the non-zero portion of components and are often limited to duration but nothing in the theorem requires that.
The Fourier series coefficients are:
where denotes the Fourier series integral.
- The pointwise product: is also -periodic, and its Fourier series coefficients are given by the discrete convolution of the and sequences:
is also -periodic, and is called a periodic convolution.
Derivation of periodic convolution
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The corresponding convolution theorem is:
| | (Eq.2) |
Derivation of Eq.2
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Functions of a discrete variable (sequences)
By a derivation similar to Eq.1, there is an analogous theorem for sequences, such as samples of two continuous functions, where now denotes the discrete-time Fourier transform (DTFT) operator. Consider two sequences and with transforms and :
The § Discrete convolution of and is defined by:
The convolution theorem for discrete sequences is:[3][4]: p.60 (2.169)
| | (Eq.3) |
Periodic convolution
and as defined above, are periodic, with a period of 1. Consider -periodic sequences and :
- and
These functions occur as the result of sampling and at intervals of and performing an inverse discrete Fourier transform (DFT) on samples (see § Sampling the DTFT). The discrete convolution:
is also -periodic, and is called a periodic convolution. Redefining the operator as the -length DFT, the corresponding theorem is:[5][4]: p. 548
| | (Eq.4a) |
And therefore:
| | (Eq.4b) |
Under the right conditions, it is possible for this -length sequence to contain a distortion-free segment of a convolution. But when the non-zero portion of the or sequence is equal or longer than some distortion is inevitable. Such is the case when the sequence is obtained by directly sampling the DTFT of the infinitely long § Discrete Hilbert transform impulse response.[A]
For and sequences whose non-zero duration is less than or equal to a final simplification is:
Circular convolution
| | (Eq.4c) |
This form is often used to efficiently implement numerical convolution by computer. (see § Fast convolution algorithms and § Example)
As a partial reciprocal, it has been shown [6]
that any linear transform that turns convolution into pointwise product is the DFT (up to a permutation of coefficients).
Convolution theorem for tempered distributions
The convolution theorem extends to tempered distributions.
Here, is an arbitrary tempered distribution:
But must be "rapidly decreasing" towards and in order to guarantee the existence of both, convolution and multiplication product. Equivalently, if is a smooth "slowly growing" ordinary function, it guarantees the existence of both, multiplication and convolution product.[7][8][9]
In particular, every compactly supported tempered distribution, such as the Dirac delta, is "rapidly decreasing". Equivalently, bandlimited functions, such as the function that is constantly are smooth "slowly growing" ordinary functions. If, for example, is the Dirac comb both equations yield the Poisson summation formula and if, furthermore, is the Dirac delta then is constantly one and these equations yield the Dirac comb identity.