Removing small objects in grayscale images with a top hat filter

This example shows how to remove small objects from grayscale images. The top-hat transform [1] is an operation that extracts small elements and details from given images. Here we use a white top-hat transform, which is defined as the difference between the input image and its (mathematical morphology) opening.

[1]

https://en.wikipedia.org/wiki/Top-hat_transform

import matplotlib.pyplot as plt

from skimage import data
from skimage import color, morphology

image = color.rgb2gray(data.hubble_deep_field())[:500, :500]

footprint = morphology.disk(1)
res = morphology.white_tophat(image, footprint)

fig, ax = plt.subplots(ncols=3, figsize=(20, 8))
ax[0].set_title('Original')
ax[0].imshow(image, cmap='gray')
ax[1].set_title('White tophat')
ax[1].imshow(res, cmap='gray')
ax[2].set_title('Complementary')
ax[2].imshow(image - res, cmap='gray')

plt.show()