3.3.9.10. Various denoising filters

This example compares several denoising filters available in scikit-image: a Gaussian filter, a median filter, and total variation denoising.

import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage import filters
from skimage import restoration

coins = data.coins()
gaussian_filter_coins = filters.gaussian(coins, sigma=2)
med_filter_coins = filters.median(coins, np.ones((3, 3)))
tv_filter_coins = restoration.denoise_tv_chambolle(coins, weight=0.1)

plt.figure(figsize=(16, 4))
plt.subplot(141)
plt.imshow(coins[10:80, 300:370], cmap='gray', interpolation='nearest')
plt.axis('off')
plt.title('Image')
plt.subplot(142)
plt.imshow(gaussian_filter_coins[10:80, 300:370], cmap='gray',
           interpolation='nearest')
plt.axis('off')
plt.title('Gaussian filter')
plt.subplot(143)
plt.imshow(med_filter_coins[10:80, 300:370], cmap='gray',
           interpolation='nearest')
plt.axis('off')
plt.title('Median filter')
plt.subplot(144)
plt.imshow(tv_filter_coins[10:80, 300:370], cmap='gray',
           interpolation='nearest')
plt.axis('off')
plt.title('TV filter')
plt.show()

Total running time of the script: ( 0 minutes 0.477 seconds)