3.3.11.10. Various denoising filtersΒΆ

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

Image, Gaussian filter, Median filter, TV filter
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 0.148 seconds)

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