Basic 2d DFT - interpreting the coefficients

In summary, the conversation discusses understanding the coefficients of a 2D DFT and how they relate to frequencies. The matrix f(a,b) is used as an example and the resulting transform is shown. The characteristic polynomial is also mentioned. Questions are asked about the coefficients at certain positions and their relationship to frequencies. The use of Wolfram Alpha to find more information is suggested. Finally, the concept of sampling rate and its effect on the frequency is discussed.
  • #1
elegysix
406
15
Thanks for any help! I'm trying to understand the coefficients of a 2d DFT.

say we've got this matrix, f(a,b)

[itex]
\left( \begin{array}{ccc}
9 & 1 & 9 \\
9 & 1 & 9 \\
9 & 1 & 9 \end{array} \right)
[/itex]

I used wolfram alpha's function, Fourier{f(a,b)}
and the transform comes back as

[itex]
\left( \begin{array}{ccc}
19 & 4-6.93i & 4+6.93i \\
0 & 0 & 0 \\
0 & 0 & 0 \end{array} \right)
[/itex]the characteristic polynomial is [itex] 19x^{2} - x^{3} [/itex]I know the coeffecient at (0,0) is an average of something, but what?
what are the other two coefficients in the top row?

If I do a matrix with a frequency in both directions, I get coefficients in the first column as well. What do they represent?

Is there enough information here to determine a function z(x,y) that approximates f(a,b)? (like a sum of sines and cosines)

three eigenvectors are given as well, if needed

thanks for your help!
 
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  • #2
Would a moderator move this to the calculus section please? 126 views and no responses yet :(

thanks
 
  • #4
I checked it out, but I don't think it answers my questions. At least not in a way that I comprehend.

How are the coefficients related to frequencies?
 
  • #5
The 1-D DFT is the dot product of the signal with a vector containing a complex sinusoid which oscillates over the indices at a variable frequency, according to the given formula.

http://reference.wolfram.com/mathematica/ref/Files/Fourier.en/3.gif

If s was 1, we'd get the zero frequency because s-1 = 0 and e^(2∏i0) = 1 (a constant).

We haven't defined a sampling rate. So, I suppose I could call (s-1) the frequency. Since (r-1)/n would be from 0 to (n-1)/n (evenly spaced sampling in the interval 0 to 1), the number of oscillations of the complex sinusoid from r = 1 to n is the oscillations of a sinusoid with frequency (s-1) over a domain of length one.
 
Last edited by a moderator:

What is a 2D DFT?

A 2D DFT, or two-dimensional discrete Fourier transform, is a mathematical algorithm that converts a 2D signal from its original spatial domain into a representation in the frequency domain. This allows for the analysis of the signal's frequency components and can be used for tasks such as filtering and compression.

How does a 2D DFT work?

A 2D DFT works by decomposing a 2D signal into a sum of complex sinusoidal functions of different frequencies and phases. These functions are called basis functions, and the coefficients of these functions are represented by the complex numbers in the resulting frequency domain representation.

What do the coefficients in a 2D DFT represent?

The coefficients in a 2D DFT represent the amplitude and phase of the corresponding basis functions in the frequency domain. The amplitude represents the strength of the frequency component, while the phase represents its position in the frequency domain.

How do you interpret the coefficients in a 2D DFT?

The coefficients in a 2D DFT can be interpreted as the contribution of each frequency component to the original signal. Higher coefficients indicate a stronger presence of that frequency component in the signal, while lower coefficients suggest a weaker presence.

What is the significance of performing a 2D DFT on an image?

Performing a 2D DFT on an image allows for the separation of the image's spatial and frequency information. This can be useful for tasks such as image compression, where high frequency components can be removed without significantly affecting the overall appearance of the image. It can also provide insight into the patterns and structures present in the image.

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