# Here we will just add a random Gaussian noise to our original image

noise_Gaussian = np.zeros((image.shape[0], image.shape[1]), dtype='uint8');

# Here a value of 64 is specified for a noise mean
# and 32 is specified for the standard deviation

cv2.randn(noise_Gaussian, 64, 32)

noisy_image = cv2.add(image, noise_Gaussian) 
cv2.imshow("Gaussian noise added - severe", noisy_image)
cv2.waitKey()

# Adding a very mild noise
cv2.randn(noise_Gaussian, 64, 8)
noisy_image1 = cv2.add(image, noise_Gaussian) 
cv2.imshow("Gaussian noise added - mild", noisy_image1)
cv2.waitKey()