We can apply a linear filter to smooth images using SciPy's ndimage module functions too. The following code snippet shows a demonstration of the results of applying the linear filters on the mandrill image degraded with impulse (salt-and-pepper) noise:
from scipy import misc, ndimage
import matplotlib.pylab as pylab
im = misc.imread('../images/mandrill_spnoise_0.1.jpg')
k = 7 # 7x7 kernel
im_box = ndimage.uniform_filter(im, size=(k,k,1))
s = 2 # sigma value
t = (((k - 1)/2)-0.5)/s # truncate parameter value for a kxk gaussian kernel with sigma s
im_gaussian = ndimage.gaussian_filter(im, sigma=(s,s,0), truncate=t)
fig = pylab.figure(figsize=(30,10))
pylab.subplot(131), plot_image(im, 'original image')
pylab.subplot(132), plot_image(im_box, 'with the box filter')
pylab.subplot(133), plot_image(im_gaussian, 'with the gaussian filter')
pylab.show()
The following screenshot shows the output of the previous code. As can be seen, the box filter of the same kernel size blurs the output image more than the Gaussian filter of same size with σ=2:
