Derivative filters computed using finite difference are quite sensitive to noise. As we saw in the last chapter, the pixels in an image that have very different intensity values from their neighbors are generally the noise pixels. In general, the more the noise the larger the change in intensity, and the stronger is the response obtained with the filters. The next code block adds some Gaussian noise to the image to see the effects on the gradient. Let us again consider a single row (row 0, precisely) of the image, and let us plot the intensity as a function of the x location:
from skimage.util import random_noisesigma = 1 # sd of noise to be addedim = im + random_noise(im, var=sigma**2)
The following ...
