Fast Fourier transform (FFT) refers to any one of a family of algorithms that can compute the discrete Fourier transform (DFT) of a signal with n elements in O(n lg n) FLOPs. The DFT is a transformation that converts a signal from its original domain (e.g., time or space) into the frequeny domain.

This article introduces the theory of the DFT and FFT and gives some examples in MATLAB.

Discrete Fourier transform

Roots of unity

Definition (Roots of unity). Let n be a positive integer. A complex number z is said to be an n-th root of unity if and only if zn = 1.

Corollary. If z is an n-th root of unity, so too is zw for any complex number w.

Proof. (zw)n = (zn)w = 1w = 1. ∎

Corollary. Let n be a positive integer and 𝜔n = e-2𝜋i/n. Then, 𝜔0, …, 𝜔n-1 are the only n-th roots of unity.

Proof. It’s easily verified that 𝜔0, …, 𝜔n-1 define n distinct n-th roots of unity. Uniqueness follows from the fundamental theorem of algebra applied to the polynomial zn - 1. ∎

Definition (Kronecker delta). The Kronecker delta 𝛿jj’ is defined to be 1 if and only if j = j’ and 0 otherwise.

Lemma. Let n be a positive integer and z ≠ 1 be an n-th root of unity. Then, z0 + … + zn-1 = 0.

Proof. The sum in question is a geometric series, with closed form (1 - zn) / (1 - z). Since z is an n-th root of unity, this is identically zero. ∎

Corollary. Let n be a positive integer and j and j’ be nonnegative integers strictly smaller than n. Then,

\[\sum_{k=0}^{n-1} \omega_n^{(j-j^\prime) k} = n \delta_{jj^\prime}.\]

Proof. If j = j’, each summand is 1. Otherwise, $\omega_n^{(j-j^\prime)}$ is an n-th root of unity and the desired result follows from the previous lemma. ∎

Forward and inverse transforms

Definition. The conjugate transpose of a complex number z = a + i b is z* = a - i b. The conjugate transpose z* of a complex vector (or matrix) z is obtained by taking the transpose of the vector (or matrix) and conjugating each entry.

Theorem. Let

\[\boldsymbol{u}^{(j)} = \frac{1}{\sqrt{n}} (\omega_n^0, \omega_n^j, \omega_n^{2j}, \ldots, \omega_n^{(n-1)j}).\]

Then, the vectors u(0), …, u(n-1) form an orthonormal basis for ℂn.

Proof. Using the previous corollary,

\(n \left \langle u^{(j)}, u^{(j^\prime)} \right \rangle = \sum_{k=0}^{n-1} \omega_n^{-j^\prime k} \omega_n^{j k} = \sum_{k=0}^{n-1} \omega_n^{(j-j^\prime) k} = n \delta_{jj^\prime}.\) ∎

The above establishes that the matrix F

(whose rows are the basis elements in the theorem) is unitary. The matrix F is called the forward transform. Because this matrix is unitary, its conjugate transpose F* is its inverse (i.e., F*F = I). As such, we call the conjugate transpose the inverse transform.

Remark. In the above, we used the scaling factor 1/√n to ensure that the matrix F was unitary, simplifying mathematical discussion. MATLAB’s definitions of forward and inverse transforms fft and ifft do not use the same scaling factor (they are F√n and F*/√n, respectively) so as to avoid the cost of an extra vector-scalar multiply in the forward transform. Software libraries have their own conventions when it comes to the scaling factors for forward and inverse transforms, so it’s best to proceed carefully!

Some immediate results

Theorem (Plancherel theorem). Let x and y be vectors in ℂn and denote by X = F x and Y = F y their forward transforms. Then, ⟨x, y⟩ = ⟨X, Y⟩.

Proof. ⟨X, Y⟩ = ⟨F x, F y⟩ = y* F* F x = y* x = ⟨x, y⟩. ∎

Corollary (Parseval’s theorem). Let x be a vector in ℂn and denote by X = F x its forward transform. Then, ⟨x, x⟩ = ⟨X, X⟩ (i.e., the original vector and its transform have the same Euclidean norm).


For integers a and b with b nonnegative, denote by a % b the least nonnegative residue of a modulo b (i.e., the usual definition of the % operator provided by most programming languages). For a vector x with n entries, we introduce the indexing convention xj = xj % n whenever j is negative or at least as large as n. Subject to this convention, we have the following results:

Definition (Circular convolution). The circular convolution of vectors x and y in ℂn is a vector x ✳︎ y with entries [x ✳︎ y]j = x0yj-0 + … + xn-1yj-(n-1).

Theorem (Convolution theorem). Let ⨂ denote the element-wise product, x and y be vectors in ℂn. Then, F(x ✳︎ y) = F x ⨂ F y and F (x ⨂ y) = F x ✳︎ F y.

The above theorem tells us that, for example, the convolution of vectors can be computed by

  1. taking their discrete Fourier transforms X and Y
  2. multiplying these element-wise to get Z = X ⨂ Y, and
  3. taking the inverse Fourier transform of Z.

In a subsequent section, we will prove that the discrete Fourier transform can be computed using only O(n lg n) FLOPs, suggesting that the above procedure is superior to naively computing the circular convolution from the formula, which requires O(n2) FLOPs.

Similar results can be established for the circular cross-correlation.

Real signals

In practice, the input to the forward transform is often a real signal (i.e., a vector in ℝn). It is useful to derive some facts about such signals.

Theorem (Conjugate symmetry). Let x be a vector in ℝn and X = F x. Then, (Xk)* = Xn-k.

Proof. First, note that X* = (F x)* = x⊺ F* = ((F*)⊺ x)⊺ where ⊺ denotes the ordinary transpose operation. Now, using the definition of F and the conjugate transpose operation, it is straightforward to establish that

from which the desired result follows.

Theorem. Let x and y be vectors in ℝn and z = x + i y. Further let X, Y, and Z be their corresponding forward transforms. Then, 2Xk = Zk + (Zn-k)* and 2Yk = Zk - (Zn-k)*.

In other words, the DFT of two real signals x and y can be computed by

  1. creating a complex signal z whose real part is x and whose imaginary part is y,
  2. computing the DFT of z, and
  3. retrieving the DFTs of x and y using the above formulas.

The proof is left as an exercise. MATLAB code for this procedure is provided below:

function [X, Y] = fft_real_signals (x, y)
% FFT_REAL_SIGNALS Computes the DFT of two real signals using one FFT.

z = x + 1.i * y;
Z = fft (z);

% Create Zr_conj, whose (j+1)-th component (MATLAB indices start at 1)
% is the conjugate of the (n-j+1)-th component of Z.
Zr_conj = conj ([Z(1) fliplr (Z(2:end))]);

X = (Z + Zr_conj) / 2.;
Y = (Z - Zr_conj) / 2.;


Fast Fourier transform

The Cooley-Tukey algorithm is the most common FFT algorithm. It re-expresses the DFT of an arbitrary composite size n = ab in terms of smaller DFTs of sizes a and b.

Radix-2 algorithm

Assuming n is even, the radix-2 algorithm corresponds to taking a = b = n / 2. The motivation comes from noting that

\[X_k = \sum_{j=0}^{n-1} x_j \omega_n^{jk} = \sum_{j = 0}^{n/2 - 1} x_{2j} \omega_n^{2jk} + \omega_n^k \sum_{j = 0}^{n/2-1} x_{2j+1} \omega_n^{2jk}\]

and hence

\[X_k = E_k + \omega_n^k O_k\]

where E and O are the DFTs of the even and odd parts of the original signal x, respectively.

By the periodicity of the DFT and

\[\omega_n^{k+n/2} = e^{-i \pi} \omega_n^k = -\omega_n^k\]

the above can be equivalently expressed as the two equations

\[X_k = E_k + \omega_n^k O_k \qquad \text{and} \qquad X_{k+n/2} = E_k - \omega_n^k O_k\]

for nonnegative integers k strictly less than n/2.

Image compression (with MATLAB code)

Image compression is one possible application of the FFT. It can be achieved by

  1. transforming the image into the frequency domain,
  2. dropping high-frequency components, and
  3. saving the result.

To view the image, one must inverse transform back to the original domain.

function [dft_R, dft_G, dft_B] = compress_image (image, ratio)
% COMPRESS_IMAGE Compresses an image.
% Inputs
% ------
% image    The original image.
% ratio    The compression ratio in (0, 1].
% Outputs
% -------
% dft_R    The DFT of the red channel.
% dft_G    The DFT of the green channel.
% dft_B    The DFT of the blue channel.

% Normalize.
image_d = double (image) / double (max (max (max (image))));

fft_image_d = fft2 (image_d);
dft_R = sparse (fft_image_d(:, :, 1));
dft_G = sparse (fft_image_d(:, :, 2));
dft_B = sparse (fft_image_d(:, :, 3));

% Size of image.
[m, n, ~] = size (image);
p = ceil (m / 2);
q = ceil (n / 2);

% Number of frequencies to remove first and second dimensions.
f  = (1. - sqrt (ratio)) / 2;
dm = round (f * m);
dn = round (f * n);

% The new image will only have r * m * n frequencies.
i = p - dm + 1 : p + dm;
j = q - dn + 1 : q + dn;
dft_R(i, :) = 0;
dft_R(:, j) = 0;
dft_G(i, :) = 0;
dft_G(:, j) = 0;
dft_B(i, :) = 0;
dft_B(:, j) = 0;

function [image] = decompress_image (dft_R, dft_G, dft_B)
% DECOMPRESS_IMAGE Returns a full representation of the image.
% Inputs
% ------
% dft_R    The DFT of the red channel.
% dft_G    The DFT of the green channel.
% dft_B    The DFT of the blue channel.
% Outputs
% -------
% image    The image.

[m, n] = size (dft_R);
image = zeros (m, n, 3);
image(:, :, 1) = real (ifft2 (full (dft_R)));
image(:, :, 2) = real (ifft2 (full (dft_G)));
image(:, :, 3) = real (ifft2 (full (dft_B)));

% Code used to create the example.
earth = imread ('earth.jpg')
ratio = 0.15;
[dft_R, dft_G, dft_B] = compress_image (earth, ratio);
imshow (decompress_image (dft_R, dft_G, dft_B));

Original image

Uncompressed image

Compressed image (15% data retention)

Compressed image