On the FFT image, the low frequency area is in the center of the image and the high frequency areas are at the corners of the image. Can someone tell me about the formation of the FFT image? For example, why is there a horizontal white line passing through the center? Also, why is the FFT image like a "sun" emitting beams?

Image -> Crop; Process-> FFT -> FFT; Process-> FFT -> Inverse FFT; Paintbrush (with black color) First, load your image. Then select the part that's only white with texture. Do FFT on this crop: You now notice a star pattern. This is the pattern to recognize when you open the image again, and do FFT on the whole thing:

Convolution of a real world image, which occurs at each and every "interface" along the optical pipeline, is effectively an irreversible process. We can't ever know all convolution factors, and therefor reconstruction of an FFT back into an image is difficult, and extreme modifications usually result in artifacts and data loss. \$\endgroup\$

Use the pen tool in the FFT window to black out all the regularly spaced bright points (except the really big one in the middle). Process -> FFT -> Inverse FFT...and that should do it. Now, one thing to keep in mind is that, apparently, when you select an operation from the Process menu, ImageJ applies it to the image window that you last selected.

Mar 2, 2015 · Below it there is a downscaled version of the image and its 2d fft. Notice the difference in spectrum amplitude in both cases. While in the first case nothing changes, in the second the spectrum actually gets better (it was weak in the first place)—I mean there is more "weight" put in the corners.

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May 20, 2014 · \$\begingroup\$ I think it is mistaken to assume that FFT isn't well understood in the image processing world. I am also not even sure it is safe to say that amateur and family photographers make up "most" image processors. I'd say most amateurs simply point, shoot, and share, without processing a all.

Oct 24, 2019 · So a 1D grayscale image A has higher frequencies than another grayscale image B if A has "finer" details. You can generalise that principle to colored 2D, 3D images. The finer the "details" of an image are the higher the frequency content of that image is. So a blue sky is low frequency in comparison with an image of a flower.

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