D F T(Discrete Fourier Transform)F F T(Fast Fourier Transform)Written by Paul BourkeJune 1993
Introduction
This document describes the Discrete Fourier Transform (DFT), that is, a Fourier Transform as applied to a discrete complex valued series. The mathematics will be given and source code (written in the C programming language) is provided in the appendices. TheoryContinuous
For a continuous function of one variable f(t), the Fourier Transform F(f) will be defined as:  and the inverse transform as
where j is the square root of -1 and e denotes the natural exponent  Discrete Consider a complex series x(k) with N samples of the form
 where x is a complex number
Further, assume that that the series outside the range 0, N-1 is extended N-periodic, that is, xk = xk+N for all k. The FT of this series will be denoted X(k), it will also have N samples. The forward transform will be defined as  The inverse transform will be defined as
Of course although the functions here are described as complex series, real valued series can be represented by setting the imaginary part to 0. In general, the transform into the frequency domain will be a complex valued function, that is, with magnitude and phase. 
The following diagrams show the relationship between the series index and the frequency domain sample index. Note the functions here are only diagrammatic, in general they are both complex valued series. 
For example if the series represents a time sequence of length T then the following illustrates the values in the frequency domain.  Notes
While the DFT transform above can be applied to any complex valued series, in practice for large series it can take considerable time to compute, the time taken being proportional to the square of the number on points in the series. A much faster algorithm has been developed by Cooley and Tukey around 1965 called the FFT (Fast Fourier Transform). The only requirement of the the most popular implementation of this algorithm (Radix-2 Cooley-Tukey) is that the number of points in the series be a power of 2. The computing time for the radix-2 FFT is proportional to 
So for example a transform on 1024 points using the DFT takes about 100 times longer than using the FFT, a significant speed increase. Note that in reality comparing speeds of various FFT routines is problematic, many of the reported timings have more to do with specific coding methods and their relationship to the hardware and operating system. Sample transform pairs and relationships
The sampling theorem (often called "Shannons Sampling Theorem") states that a continuous signal must be discretely sampled at least twice the frequency of the highest frequency in the signal. More precisely, a continuous function f(t) is completely defined by samples every 1/fs (fs is the sample frequency) if the frequency spectrum F(f) is zero for f > fs/2. fs/2 is called the Nyquist frequency and places the limit on the minimum sampling frequency when digitising a continuous signal. If x(k) are the samples of f(t) every 1/fs then f(t) can be exactly reconstructed from these samples, if the sampling theorem has been satisfied, by  where
Normally the signal to be digitised would be appropriately filtered before sampling to remove higher frequency components. If the sampling frequency is not high enough the high frequency components will wrap around and appear in other locations in the discrete spectrum, thus corrupting it. The key features and consequences of sampling a continuous signal can be shown graphically as follows. Consider a continuous signal in the time and frequency domain.
Sample this signal with a sampling frequency fs, time between samples is 1/fs. This is equivalent to convolving in the frequency domain by delta function train with a spacing of fs. 
If the sampling frequency is too low the frequency spectrum overlaps, and become corrupted. 
Another way to look at this is to consider a sine function sampled twice per period (Nyquist rate). There are other sinusoid functions of higher frequencies that would give exactly the same samples and thus can't be distinguished from the frequency of the original sinusoid. Appendix A. DFT (Discrete Fourier Transform)
/*
Direct fourier transform
*/
int DFT(int dir,int m,double *x1,double *y1)
{
long i,k;
double arg;
double cosarg,sinarg;
double *x2=NULL,*y2=NULL;
x2 = malloc(m*sizeof(double));
y2 = malloc(m*sizeof(double));
if (x2 == NULL || y2 == NULL)
return(FALSE);
for (i=0;i<m;i++) {
x2[i] = 0;
y2[i] = 0;
arg = - dir * 2.0 * 3.141592654 * (double)i / (double)m;
for (k=0;k<m;k++) {
cosarg = cos(k * arg);
sinarg = sin(k * arg);
x2[i] += (x1[k] * cosarg - y1[k] * sinarg);
y2[i] += (x1[k] * sinarg + y1[k] * cosarg);
}
}
/* Copy the data back */
if (dir == 1) {
for (i=0;i<m;i++) {
x1[i] = x2[i] / (double)m;
y1[i] = y2[i] / (double)m;
}
} else {
for (i=0;i<m;i++) {
x1[i] = x2[i];
y1[i] = y2[i];
}
}
free(x2);
free(y2);
return(TRUE);
}
Appendix B. FFT (Fast Fourier Transform)
/*
This computes an in-place complex-to-complex FFT
x and y are the real and imaginary arrays of 2^m points.
dir = 1 gives forward transform
dir = -1 gives reverse transform
*/
short FFT(short int dir,long m,double *x,double *y)
{
long n,i,i1,j,k,i2,l,l1,l2;
double c1,c2,tx,ty,t1,t2,u1,u2,z;
/* Calculate the number of points */
n = 1;
for (i=0;i<m;i++)
n *= 2;
/* Do the bit reversal */
i2 = n >> 1;
j = 0;
for (i=0;i<n-1;i++) {
if (i < j) {
tx = x[i];
ty = y[i];
x[i] = x[j];
y[i] = y[j];
x[j] = tx;
y[j] = ty;
}
k = i2;
while (k <= j) {
j -= k;
k >>= 1;
}
j += k;
}
/* Compute the FFT */
c1 = -1.0;
c2 = 0.0;
l2 = 1;
for (l=0;l<m;l++) {
l1 = l2;
l2 <<= 1;
u1 = 1.0;
u2 = 0.0;
for (j=0;j<l1;j++) {
for (i=j;i<n;i+=l2) {
i1 = i + l1;
t1 = u1 * x[i1] - u2 * y[i1];
t2 = u1 * y[i1] + u2 * x[i1];
x[i1] = x[i] - t1;
y[i1] = y[i] - t2;
x[i] += t1;
y[i] += t2;
}
z = u1 * c1 - u2 * c2;
u2 = u1 * c2 + u2 * c1;
u1 = z;
}
c2 = sqrt((1.0 - c1) / 2.0);
if (dir == 1)
c2 = -c2;
c1 = sqrt((1.0 + c1) / 2.0);
}
/* Scaling for forward transform */
if (dir == 1) {
for (i=0;i<n;i++) {
x[i] /= n;
y[i] /= n;
}
}
return(TRUE);
}
Modification by Peter Cusack that uses the MS complex type
fft_ms.c.References
Fast Fourier Transforms
Fast Fourier Transforms: Algorithms
Fast Fourier Transforms and Convolution Algorithms
Digital Signal Processing
The following briefly describes how to perform 2 dimensional Fourier
transforms. Source code is given at the end and an example is presented
where a simple low pass filtering is applied to an image. Filtering in
the spatial frequency domain is advantageous for the same reasons as
filtering in the frequency domain is used in time series analysis,
the filtering is easier to apply and can be significantly faster.
In the most general situation a 2 dimensional transform takes a complex
array. The most common application is for image processing where each
value in the array represents to a pixel, therefore the real value
is the pixel value and the imaginary value is 0.
2 dimensional Fourier transforms simply involve a number of 1 dimensional
Fourier transforms. More precisely, a 2 dimensional transform
is achieved by first transforming each row, replacing
each row with its transform and then transforming each column, replacing
each column with its transform. Thus a 2D transform of a 1K by 1K image
requires 2K 1D transforms. This follows directly from the definition
of the Fourier transform of a continuous variable or the discrete Fourier
transform of a discrete system.
The transform pairs that are commonly derived in 1 dimension can also
be derived for the 2 dimensional situation. The 2 dimensional pairs can
often be derived simply by considering the procedure of applying
transforms to the rows and then the columns of the 2 dimensional array.
Note
The above example has had the quadrants reorganised so as to place
DC (freq = 0) in the center of the image. The default organisation
of the quadrants from most FFT routines is as below
In order to perform FFT (Fast Fourier Transform) instead of the
much slower DFT (Discrete Fourier Transfer) the image must be
transformed so that the width and height are an integer power of 2.
This can be achieved in one of two ways, scale the image up to the
nearest integer power of 2 or zero pad to the nearest integer power
of 2. The second option was chosen here to facilitate comparisons with
the original. The resulting image is 256 x 256 pixels.
Image processing can now be performed (for example filtering) and
the image converted back to the spatial domain.
For example low pass filtering involves reducing the high frequency
components (those radially distant from the center of the above image).
Two examples using different cut-off frequencies are illustrated below.
|
