Fourier transform sum of two images

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Discussion Overview

The discussion revolves around the Fourier Transform (FT) in the context of image processing, specifically focusing on the relationship between the sum of two images and their Fourier representations. Participants explore concepts related to frequency components, image reconstruction, and the mathematical underpinnings of the FT.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants propose that the FT decomposes images into individual frequency components and question whether the sum of two images corresponds to a specific image.
  • Others express confusion about the relevance of certain images to the FT and seek clarification on the connection between the images and the FT process.
  • A participant mentions that the FT is a linear map, suggesting that the FT of a sum of functions equals the sum of their FTs.
  • There is a discussion about the representation of spatial and frequency domains, with one participant seeking to understand which images correctly illustrate this transformation.
  • Some participants suggest that the bottom image may represent a pair of Fourier transform conjugates, noting its symmetry in k-space.
  • Mathematical representations involving delta functions are introduced to justify the appearance of the images, although some participants express a lack of understanding of these concepts.
  • One participant acknowledges their limited mathematical background and expresses concern about their ability to grasp the underlying principles of the FT.
  • It is noted that the bottom image may represent the amplitude or power spectrum of the original image, but the importance of the corresponding phase spectrum is also highlighted.

Areas of Agreement / Disagreement

Participants exhibit a mix of agreement and disagreement regarding the interpretations of the images and their relationship to the FT. Some concepts are clarified, but no consensus is reached on the correctness of the representations or the mathematical details involved.

Contextual Notes

Participants express varying levels of mathematical understanding, with some relying on conceptual explanations while others reference specific mathematical constructs like delta functions. The discussion reflects a range of assumptions and interpretations regarding the FT and its application to image processing.

Who May Find This Useful

This discussion may be of interest to individuals learning about Fourier Transforms, image processing, or those seeking to understand the mathematical foundations of these concepts without a strong background in mathematics.

BobP
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The FT decomposes images into its individual frequency components
In its absolute crudest form, would the sum of these two images (R) give the L image?
 

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The sum should also have the vertical grey arm. But I just don't see how those pictures connect to the subject of FT.
 
blue_leaf77 said:
The sum should also have the vertical grey arm. But I just don't see how those pictures connect to the subject of FT.
I thought the FT was about decomposing images into different frequencies. So I was showing the inverse of what I thought the FT and asking if it was correct as it was easier for me to show it this way.
Is what I showed not related?
 
You can use those pictures as an analogy to an FT, because FT is about summing functions of the form ##e^{ik_xx}## (for 1D) and ##e^{ik_xx}e^{ik_yy}## (for 2D) having certain amplitude distribution.
 
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What are R and L?
 
I'm not exactly sure what you are asking,. But, the Fourier transform is a linear map. So F{f(x)+g(x)} = F{f(x)} +F{g(x)}.
 
pixel said:
What are R and L?
Right and left (referrring to the images)
 
blue_leaf77 said:
You can use those pictures as an analogy to an FT, because FT is about summing functions of the form ##e^{ik_xx}## (for 1D) and ##e^{ik_xx}e^{ik_yy}## (for 2D) having certain amplitude distribution.
Ok, in addition then which of the two examples shown correctly represents the transformation between the spatial domain (right) and domain (left)

I am only learning the FT to gain a very crude understanding of how image reconstruction is done. As I do not have a physics background the course organiers are not teaching the maths
 

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BobP said:
Ok, in addition then which of the two examples shown correctly represents the transformation between the spatial domain (right) and domain (left)
To me, the bottom picture seems to correspond to a pair of Fourier transform conjugates (can you see why?).
 
  • #10
blue_leaf77 said:
To me, the bottom picture seems to correspond to a pair of Fourier transform conjugates (can you see why?).
Presumably because it is symmetrical about the centre of k-space.
But as the image only displays a single frequency I wasn't sure if I only needed to display two conugate pairs or lots

By your answer am I correct in assuming you think the bottom image is correct then?
 
  • #11
BobP said:
Presumably because it is symmetrical about the centre of k-space.
But as the image only displays a single frequency I wasn't sure if I only needed to display two conugate pairs or lots

By your answer am I correct in assuming you think the bottom image is correct then?
A way of justifying the bottom images is to do the math. The right picture of the bottom pair looks like it being composed of three delta functions. Mathematically, it reads
$$
f(x,y) = \delta(y) (\delta(x+a) + \delta(x) + \delta(x-a))
$$
Now Fourier transform ##f(x,y)## and see if you will get something that resembles the left picture.
 
  • #12
blue_leaf77 said:
A way of justifying the bottom images is to do the math. The right picture of the bottom pair looks like it being composed of three delta functions. Mathematically, it reads
$$
f(x,y) = \delta(y) (\delta(x+a) + \delta(x) + \delta(x-a))
$$
Now Fourier transform ##f(x,y)## and see if you will get something that resembles the left picture.
Hi. Like I said I don't know any of the maths so I am learning everything conceptually.
So I don't really know what you mean by 3 delta functions :(
 
  • #13
BobP said:
Like I said I don't know any of the maths so I am learning everything conceptually.
FT is one subject of math, there is no other way to learn FT except by learning the maths.
Delta functions is a mathematical object usually used to represent a zero-dimensional point. In reality obviously there is no such object, however if a pinhole is much smaller compared to the wavelength of light illuminating it (in the case of the diffraction of light), it can be modeled as a delta function.
 
  • #14
blue_leaf77 said:
FT is one subject of math, there is no other way to learn FT except by learning the maths.
Delta functions is a mathematical object usually used to represent a zero-dimensional point. In reality obviously there is no such object, however if a pinhole is much smaller compared to the wavelength of light illuminating it (in the case of the diffraction of light), it can be modeled as a delta function.
I see. well thank you for the confirmation but as I have a very limited knowledge of calculus it seems like it'll be a long time before I can prove why the bottom image is correct.

Thanks again for your help though :)
 
  • #15
The bottom image looks like the correct amplitude or power spectrum of the original image, which is based on the magnitude of the complex Fourier transform. However, you won't be able to reconstruct an image without a corresponding phase spectrum that comes from the argument of the same transform.
 
  • #16
boneh3ad said:
The bottom image looks like the correct amplitude or power spectrum of the original image, which is based on the magnitude of the complex Fourier transform. However, you won't be able to reconstruct an image without a corresponding phase spectrum that comes from the argument of the same transform.
thanks :)
 

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