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I have bunch of gray-scale input images with blob-noise-like parts (however, they are not an actual noise):

Example of one such image: enter image description here

I need to keep only areas inside red squares and remove the rest as much as possible. Which filter or process is best for this task?

I have tried:

  • Convolution filters but none of them worked or required threshold that can vary image form image
  • Gabor filters, but they are hard to set up correctly.
  • In the Fourier domain, I cannot see any usefulness.
  • Binarization and morphology remove the parts in boxes.
  • I have also come up with an idea to use Hough line detection, but it is not finding the correct line parts (or I have an incorrect implementation).

Machine learning could solve this, but I don't want to use it, since I don't have annotated data.

Do you have any idea?

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1 Answer 1

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If that's really representative, then good ol' rules-based machine vision processing would work a treat. There's probably names for these algorithms that I don't know:

It looks like your "keep" regions are more than 50% full of light pixels; you could segment to 0 and 1, low-pass, then keep the blobs that are above 50%.

Alternately, you could segment to 0 and 1, dialate everything by half the width of the strokes in your "keep" blobs, then erode everything by twice the width of those strokes. What should be left is a pretty good estimate of your "blobs to keep". Then window those, and use them to edit the original image.

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