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My main point on the noise removal was to have a cleaner image so it would be easier to detect objects. However, as I tried to find a solution for the problem, I realized that it was unrealistic to remove all noise from the image, since most of the image is actually noise.. So I had to find the objects despite the noise. Here is my aproach

1 - Initial image

2 - Background subtraction followed by opening operation to smooth noise

3 - Binary threshold

4 - Morphological operation close to make sure object has no edge discontinuities (necessary for thin objects)

5 - Fill holes + opening morphological operations to remove small noise blobs

6 - Detection