Timeline for Removing horizontal noise artefacts from a (SEM) image
Current License: CC BY-SA 4.0
16 events
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Jun 13, 2019 at 15:39 | vote | accept | Letshin | ||
Jun 13, 2019 at 15:38 | vote | accept | Letshin | ||
Jun 13, 2019 at 15:39 | |||||
Jun 13, 2019 at 15:38 | vote | accept | Letshin | ||
Jun 13, 2019 at 15:38 | |||||
Jun 10, 2019 at 12:09 | vote | accept | Letshin | ||
Jun 10, 2019 at 12:11 | |||||
Jun 10, 2019 at 6:55 | answer | added | Mark R | timeline score: 6 | |
Jun 8, 2019 at 19:29 | answer | added | halirutan | timeline score: 13 | |
Jun 8, 2019 at 18:42 | comment | added | Carl Lange |
Just a thought, but you can attempt to model the noise with something like MedianFilter[i, {0, 50}] , (across 0 vertical, 50 horizontal pixels) which you could then try to subtract from the image.
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Jun 7, 2019 at 15:36 | history | edited | Letshin | CC BY-SA 4.0 |
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Jun 7, 2019 at 15:01 | history | tweeted | twitter.com/StackMma/status/1137011583215968258 | ||
Jun 7, 2019 at 12:45 | comment | added | Carl Lange |
Probably overthinking it, but you could transfer learn this U-Net on your working data to segment the image. But more than likely you can just solve this using some smart filtering (have a look at ImagePyramid )
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Jun 7, 2019 at 12:41 | history | edited | Carl Lange | CC BY-SA 4.0 |
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Jun 7, 2019 at 12:09 | history | edited | Carl Lange | CC BY-SA 4.0 |
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Jun 7, 2019 at 11:03 | comment | added | Letshin | Nope, sadly. I took a few images during that run. The last image I've attached in the question is an example of this - and there were no artefacts there. There must have been some fluctuations during the scanning process, maybe because the sample was (somewhat) magnetic. | |
Jun 7, 2019 at 11:01 | comment | added | Carl Lange | Does the noise manifest when you take a blank image? If so, you could take a blank image of the noise and subtract that from your images. | |
Jun 7, 2019 at 10:56 | history | edited | Letshin | CC BY-SA 4.0 |
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Jun 7, 2019 at 10:49 | history | asked | Letshin | CC BY-SA 4.0 |