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Found 6 results

  1. Hi I am troubled with Scanlines in my Scans of Old Photos. Scanning them with 1200 dpi. And just for Print or Displaying they are good enough. But as soon as i work with them (like sharpening) Scanlines become visible. Now to my Problem. It only happens in certain Areas of wihte and grey. So using "FFT Denoise" works but i get Banding on other Areas. Zoomed in With "Detect Horizontal Edges" i can make the worst parts visible. Zoomed in But how do i proceed from here out on to only use the Filter on this Areas in the Final Picture? And is there another Option than the FFT Denois Filter. Because i have a couple of hundred of Pictures and FTT does not work in combination with the Macro Feature for Batch processing. Hope someone got an Idea
  2. I am running Affinity Photo 1.6.1.93 on Win 7 Pro 64 bit. A photo scan with prominent paper texture stipples - circular bumps - throughout in regular arrays, failed to generate an FFT spectrum after running FFT Denoise. Procedure: I tried full photo, then later a smaller all white area. I selected FFT Denoise > Spectrum panel > Apply > FFT Denoise appeared to run > result: nothing, all black, no FFT spectrum generated. What am I doing wrong?
  3. The FFT Denoise filter seems to be broken for me in Photo beta5. When I try to use it, in the Before/After view, the After view just shows an empty canvas chequer pattern. And if I click Apply, that’s exactly what get, an empty canvas!
  4. Please add the FFT Denoise Filter for Affinity Photo iPad version.
  5. How do I zoom the FFT display in Affinity Photo Windows version. All the help says is hold down the Alt key??? Nothing I have tried zooms the FFT display. Help
  6. Hello, I have just watched a tutorial in this topic here: which is helpful, but doesn’t cover the whole story. 1st of all, if I press the alt key I cannot zoom out in the panel. I work on a "german" machine, is the key different? But what bothers me more is the fact that I do not understand the window that pops up after calling the filter. It displays some very confusing stuff (see attached example). Can you tell me how to read the greyscale image?
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