Solving Image Processing Problem: Fourier Transform and Image Dimensions

AI Thread Summary
The discussion revolves around the Fourier transform of an image and its relationship to a larger image with doubled dimensions. The initial proposed solution, g(x,y) = 0.25f(x/2,y/2), is incorrect due to the misinterpretation of pixel values, particularly when x or y are odd. The correct approach involves understanding that the larger image g(x,y) includes interpolated points between the original image's pixels, a process known as zero-padding. The final consensus emphasizes that while the proposed formula works for even dimensions, interpolation must be acknowledged for odd dimensions. This highlights the importance of precise definitions in image processing.
mr_k
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hi!
i need help with problem:
Let F(u,v) be the Fourier transform of an M x N image f(x,y). Let g(x,y) be an image of dimensions (2 M) x (2 N) whose Fourier transform G(u,v) is defined as follows:
quest2.gif

What does the image g(x,y) look like in terms of f(x,y)?

my solution : g(x,y)=0.25f(x/2,y/2).

Is that true?

thanks :)
 
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No, you're on the wrong track. You have an image that has looks basically the same but has twice (in this case) as many points. These extra points are interpolated between the points of the original f. This is known as zero-padding. See if you can work it out from the DFT expressions.
 
You are not completely on the wrong track, but your math solution is wrong, because the functions f(x,y) etc are only defined when x and y are integers. You can't talk about "half a pixel", so f(x/2, y/2) doesn't mean anything if x or y are odd numbers.

On the other hand if you say what your formula means "in words" rather than as a math formula, it does describe what the image "looks like".
 
mr_k said:
hi,my solution:

What's the problem with the answer?

thanks.

Your answer is fine for even x and y.
But what happens if x or y is odd?
 
ok,So what's the right answer?
 
Your answer is right, but you should add that if x or y is odd, that the value is effectively interpolated.
 
thanks!
 
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