Informational content in 2D discrete Fourier transform

In summary, the discrete Fourier transform (DFT) of a one-dimensional signal has a complex conjugate symmetry, where the second half of the result is the complex conjugate of the first half. This means that throwing out the second half of the result does not result in any data loss, as the entire signal can be recreated from just the first half by calculating its complex conjugate and stitching it together. However, this is true only for a purely real signal with a symmetry condition. In the case of a two-dimensional DFT, the upper right and lower left quadrants are complex conjugate symmetric and can be derived from each other. The top left and lower right quadrants may contain additional information, but it is unclear if they can
  • #1
timelessmidgen
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When you do a discrete Fourier transform (DFT) of a one-dimensional signal, I understand that the second half of the result is the complex conjugate of the first half. If you threw out the second half of the result, you're not actually losing any data and you would be able to recreate the entire signal from just the first half after calculating its complex conjugate and stitching it together (right?)

I get confused when it comes to the two dimensional DFT. For the purposes of this conversation I'm assuming the result of the DFT is unshifted (IE all four corners in the two dimensional result correspond to low frequency signals, as opposed to the center as it would be in a shifted result.) When I think about the results I thought all the actual information would be contained within the lower left quadrant, and indeed I know that the numbers in the upper right quadrant will simply be complex conjugates of the numbers in the lower left quadrant. I also know that numbers in the upper left quadrant will be complex conjugates of the numbers in the lower right quadrant, but what is their relation to the numbers in the lower left quadrant (the 'important' quadrant in my mind). Can you recreate the upper left and lower right quadrants if all you have is the lower left quadrant? Or do they actually contain more information?
 
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  • #2
Your first statement is true only for a purely real signal, and you have left off the symmetry condition. The correct statement is that the FT of a real signal is Hermitian, that is, complex conjugate symmetric.
 
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  • #3
marcusl said:
Your first statement is true only for a purely real signal, and you have left off the symmetry condition. The correct statement is that the FT of a real signal is Hermitian, that is, complex conjugate symmetric.
Ah ok thanks, that's good to know. So in the case of a real signal do the top left and lower right quadrants contain additional information? Or are they derivable from the lower left quadrant?
 
  • #4
The upper right and lower left quadrants are complex conjugate symmetric, so can be derived from each other.
 
  • #5
Thanks marcusl, but what I'm asking is if they (the top left and lower right quadrants) can be derived not from each other, but from the lower left quadrant. IE, for a purely real signal is the full informational content of the DFT contained within the lower left quadrant?

It's easy for me to physically interpret the lower left quadrant - it's the magnitude and phase of the frequencies present in the original signal. As we move right and up within the quadrant, the pixels correspond to progressively higher frequencies until we get to the edges of the quadrant (which are the centerlines of the full DFT grid) which correspond to the highest frequency. This sounds like a complete description of the information to me, hence the question of whether the top left and lower right quadrants contain additional information. If not, I would think that they must be derivable from the lower left quadrant by itself.
 
  • #6
You have the information needed to answer this question. What do you think?
 

1. What is a 2D discrete Fourier transform?

A 2D discrete Fourier transform is a mathematical technique used to analyze and represent a 2D signal or image as a combination of sinusoidal functions. It converts a signal from its original domain (such as time or space) to a frequency domain, where the amplitude and phase of each frequency component can be identified.

2. How is the informational content of a signal represented in a 2D discrete Fourier transform?

In a 2D discrete Fourier transform, the informational content of a signal is represented by the amplitude and phase of each frequency component. The amplitude represents the strength of the frequency component, while the phase represents the position of the component in the signal.

3. What are the applications of 2D discrete Fourier transform in image processing?

The 2D discrete Fourier transform has various applications in image processing, such as image compression, image filtering, and image enhancement. It is also used in pattern recognition, image registration, and image reconstruction.

4. How is the 2D discrete Fourier transform computed?

The 2D discrete Fourier transform is computed using a mathematical algorithm called the Fast Fourier Transform (FFT). The algorithm breaks down the 2D signal into smaller components and calculates the Fourier transform of each component. The results are then combined to obtain the final 2D discrete Fourier transform.

5. Can the 2D discrete Fourier transform be applied to non-square images?

Yes, the 2D discrete Fourier transform can be applied to non-square images. However, the image must be resized to a square shape before performing the transformation to avoid distortions in the frequency domain. Some techniques, such as zero-padding, can also be used to handle non-square images without resizing them.

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