A difuculdades I have to use some filters in G'MIC is that the preview does not give them later with the generated image.
I've discussed in the past that fact in a post here on GC.

In the case of the filter used by Sallyanne (G'MIC - Ian's noise reduction), had tried to improve the image using it, but the preview did not get anything even close to this topic presented by Sally. Then discarded.

But more strange is when I use the same settings as another user, and regardless of what the preview shows, the end result ends up being different than expected.
In the specific case, play the same settings Rod, but my image is in no way seemed to him that after applying the filter.

This varies from machine to machine?
In your case, using the same settings, the final image approximates the same look?

The result obtained with the same settings, presents much more residual noise. Some very visible in Annex

Removing this is done by a process called descreening https://sites.google.com/a/colgate.edu/ ... escreening

Here is my manual descreening effort using fft/inverse fft:


There is also a script: http://registry.gimp.org/node/24411

That is a front end to this fft plugin: http://registry.gimp.org/node/19596

-Rob A>

The gmic fft is a little strange but in my opinion has a more clear display over the regular (and old) gimp plugin. Both work however.

My demo, somewhat old, on using gmic fft

http://www.youtube.com/watch?v=30XaCfM2QGg

I think Ronounours has already done an excellent recovery, here are some comments anyway

1./ re. why is FFT difficult inside GIMP
using fft and inverse fft must be with a floating point precision as the quantities produces are well outside the 0-255 values that 8-bits per channel can represent.

best way to play is with the command line but you can mess around in the "custom filter" and preview too.

2./ re. why is "anisotropic" good
We start by noticing that the screening frequencies and other noise seem to be mostly at high frequencies, so a first estimate of noise removal can be a low-pass or a blur but often that loses edge information too , by using anisotropic filters we can preserve edges, and it is another optimisation as to "how much"
sometimes it is nice to visualise the noise itself and instead recover edge data to put back into the image .. see under Testing / Iain Fergusson in GMIC menu for examples of "detail recovery".

In this case there is also a problem from JPEG compression quantizing values in Y'CbCr colourspace and 4:2:2 resolution which gives a lot of cross-talk in chroma noise from the original scan of CMYK printed material where there are four distinct grid orientations - see the "star" shape still visible in noise is actually in all channels - if we could start with an original uncompressed scan it might be that C M Y K split could be treated separately.

Anyway on my machine I ran this command line to get the image uploaded here ..

cd C:\msys\home\James\gmic-1.5.7.0\src
set PATH=c:\msys\bin;%PATH%

C:\msys\home\James\gmic-1.5.7.0\src>gmic.exe c:\Users\James\Pictures\20130904fft.jpg --b 5 --[0] [1] --smooth[0] 40,0.2,0.8 --[0] [2] -+[1,2] --fftpolar -rm[3,5] -log[2,3] -*[2,3] 15 -+[0] 127 -a[1,3] y -a[0,2] y -a[0,1] x -c 0,255

Explanation of the steps:
--b 5 --[0] [1] # split high from low spatial frequency components
--smooth[0] 40,0.2,0.8 # recover some edge information from high component
--[0] [2] -+[1,2] # subtract and add to update original estimates
--fftpolar -rm[3,5] # visualise the power spectrum ignoring the phase
-log[2,3] -*[2,3] 15 -+[0] 127 # scaled log for fft and grain-extract for noise
-a[1,3] y -a[0,2] y -a[0,1] x -c 0,255 # combine all four into a big 8-bit image

excellent RobA !

i was wondering if it is this moire you get from scanning, but it looked too odd for me
so thank you for the educational link !


also thank you @gerard82 for the explanation !

Wow!!
How much learning!
This topic has been another class image editing.
After seeing the video rich2005, could use the FFT, and the results were amazing!
To all who were involved in this matter, thank you very much!

Yes, thank you. A very educational thread.
Anyway I had never noticed these patterns in my scans. Sometimes I just needed to apply deinterlacing.

Ok, now I think I begin to understand:
https://secure.flickr.com/groups/gmic/d ... 462331405/

I had not paid much attention to what jayprich was written.
Interesting filter.