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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.
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)
Jun 7, 2019 at 12:41 history edited Carl Lange CC BY-SA 4.0
edited title
Jun 7, 2019 at 12:09 history edited Carl Lange CC BY-SA 4.0
added 58 characters in body
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
added 166 characters in body
Jun 7, 2019 at 10:49 history asked Letshin CC BY-SA 4.0