The following code block shows how the SciPy signal module's fftconvolve() function can be used to run the convolution in the frequency domain (internally by just a multiplication and the convolution theorem):
im = np.mean(misc.imread('../images/mandrill.jpg'), axis=2) print(im.shape)# (224, 225)gauss_kernel = np.outer(signal.gaussian(11, 3), signal.gaussian(11, 3)) # 2D Gaussian kernel of size 11x11 with σ = 3im_blurred = signal.fftconvolve(im, gauss_kernel, mode='same')fig, (ax_original, ax_kernel, ax_blurred) = pylab.subplots(1, 3, figsize=(20,8))ax_original.imshow(im, cmap='gray')ax_original.set_title('Original', size=20)ax_original.set_axis_off()ax_kernel.imshow(gauss_kernel) ...
