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Inpainting [1] is the process of reconstructing lost or deteriorated parts of images and videos.

The reconstruction is supposed to be performed in fully automatic way by exploiting the information presented in non-damaged regions.

In this example, we show how the masked pixels get inpainted by inpainting algorithm based on ‘biharmonic equation’-assumption [2] [3] [4].

[1]Wikipedia. Inpainting
[2]Wikipedia. Biharmonic equation
[3]N.S.Hoang, S.B.Damelin, “On surface completion and image inpainting by biharmonic functions: numerical aspects”, arXiv:1707.06567
[4]C. K. Chui and H. N. Mhaskar, MRA Contextual-Recovery Extension of Smooth Functions on Manifolds, Appl. and Comp. Harmonic Anal., 28 (2010), 104-113, DOI:10.1016/j.acha.2009.04.004
import numpy as np
import matplotlib.pyplot as plt

from skimage import data
from skimage.restoration import inpaint

image_orig = data.astronaut()[0:200, 0:200]

# Create mask with three defect regions: left, middle, right respectively
mask = np.zeros(image_orig.shape[:-1])
mask[20:60, 0:20] = 1
mask[160:180, 70:155] = 1
mask[30:60, 170:195] = 1

# Defect image over the same region in each color channel
image_defect = image_orig.copy()
for layer in range(image_defect.shape[-1]):
    image_defect[np.where(mask)] = 0

image_result = inpaint.inpaint_biharmonic(image_defect, mask,

fig, axes = plt.subplots(ncols=2, nrows=2)
ax = axes.ravel()

ax[0].set_title('Original image')


ax[2].set_title('Defected image')

ax[3].set_title('Inpainted image')

for a in ax:


Total running time of the script: ( 0 minutes 3.260 seconds)

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