11–20 of 34 posts
I wonder what would be the more systematic approach than manually painting over the fft image? That constellation of peaks in fft should be reasonably easy to recognize (semi-)automatically, then you'd need to figure out good mask for those.. is it a circular blob, or maybe diamond/star shape? And what size is best? Etc etc.
I wonder what would be the more systematic approach than manually painting over the fft image? That constellation of peaks in fft should be reasonably easy to recognize (semi-)automatically, then you'd need to figure out good mask for those.. is it a circular blob, or maybe diamond/star shape? And what size is best? Etc etc.
It has been a long time since I have done anything in this domain (20+ years), but if I understand correctly, the original article author is essentially filtering out high frequency signals by painting over the bright FFT spots, essentially creating a localized low-pass filter or maybe band-pass filter. FFT essentially just transforms data into its frequency domain, so I assume the representation shown in the article…
I wonder what would be the more systematic approach than manually painting over the fft image? That constellation of peaks in fft should be reasonably easy to recognize (semi-)automatically, then you'd need to figure out good mask for those.. is it a circular blob, or maybe diamond/star shape? And what size is best? Etc etc.
Earlier quoted context omitted.
It has been a long time since I have done anything in this domain (20+ years), but if I understand correctly, the original article author is essentially filtering out high frequency signals by painting over the bright FFT spots, essentially creating a localized low-pass filter or maybe band-pass filter. FFT essentially just transforms data into its frequency domain, so I assume the representation shown in the article…
I dont believe that by painting over the bright FFT spots you are necessarily removing the high frequencies. Instead, you are removing the strong frequencies from the image. The strong frequencies cause peaks in the FFT image. Its the repetitive nature of the moire effect that cause peaks in the FFT image.
Earlier quoted context omitted.
It has been a long time since I have done anything in this domain (20+ years), but if I understand correctly, the original article author is essentially filtering out high frequency signals by painting over the bright FFT spots, essentially creating a localized low-pass filter or maybe band-pass filter. FFT essentially just transforms data into its frequency domain, so I assume the representation shown in the article…
I dont believe that by painting over the bright FFT spots you are necessarily removing the high frequencies. Instead, you are removing the strong frequencies from the image. The strong frequencies cause peaks in the FFT image. Its the repetitive nature of the moire effect that cause peaks in the FFT image.
you can think of it as a polar plot where: radius = frequency, angle = angle (as in points along the horizontal direction in the FFT correspond to e.g. vertical stripes in the image), brightness = power
There is an imaginary part to the FFT that contains the phase-shifts needed to add everything back up into the original image. You may have noticed that the "FFT of the image" is symmetric, so something else should be needed to compute the inverse.
Earlier quoted context omitted.
It has been a long time since I have done anything in this domain (20+ years), but if I understand correctly, the original article author is essentially filtering out high frequency signals by painting over the bright FFT spots, essentially creating a localized low-pass filter or maybe band-pass filter. FFT essentially just transforms data into its frequency domain, so I assume the representation shown in the article…
I dont believe that by painting over the bright FFT spots you are necessarily removing the high frequencies. Instead, you are removing the strong frequencies from the image. The strong frequencies cause peaks in the FFT image. Its the repetitive nature of the moire effect that cause peaks in the FFT image.
So all the people who insisted nothing could be done were just ignorant of what could be done?
[1]: https://docs.gimp.org/2.10/en/plug-in-wavelet-decompose.html
So I tried it on his image and here's how it looks with default settings:
https://demo.intellique.org/nextcloud/index.php/s/SsgWGczo6G...
Much less work :)
When you get Moiré from the sensor of a camera or scanner, then there is nothing much you can do. But extreme Moiré effect from resizing is not really Moiré. It's aliasing. And it typically happens because the resizing happens in a non-linear colorspace and/or with a poor kernel. Unfortunately this mistake is quite common in many image processing software.
