Dilation is another basic morphological operation that expands the size of the foreground objects, smooths object boundaries, and closes holes and gaps in a binary image. This is a dual operation of erosion. The following code snippet shows how to use the binary_dilation() function on Tagore's binary image with a disk structuring elements of different sizes:

from skimage.morphology import binary_dilation, disk
from skimage import img_as_float
im = img_as_float(imread('../images/tagore.png'))
im = 1 - im[...,3]
im[im <= 0.5] = 0
im[im > 0.5] = 1
pylab.gray()
pylab.figure(figsize=(18,9))
pylab.subplot(131)
pylab.imshow(im)
pylab.title('original', size=20)
pylab.axis('off')
for d in range(1,3):
    pylab.subplot(1,3,d+1)
    im1 = binary_dilation(im, disk(2*d))
    pylab.imshow(im1)
    pylab.title('dilation with disk size ' + str(2*d), size=20)
    pylab.axis('off')
pylab.show() 

The following screenshot shows the output of the previous code block. As can be seen, with a smaller size structuring element, a few details (that are treated as background or gaps) from the face got removed and, with the larger size disk, all of the small gaps were filled: