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 https://en.wikipedia.org/wiki/Inpainting |

[2] | Wikipedia. Biharmonic equation https://en.wikipedia.org/wiki/Biharmonic_equation |

[3] | N.S.Hoang, S.B.Damelin, “On surface completion and image inpainting by biharmonic functions: numerical aspects”, https://arxiv.org/abs/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,
multichannel=True)
fig, axes = plt.subplots(ncols=2, nrows=2)
ax = axes.ravel()
ax[0].set_title('Original image')
ax[0].imshow(image_orig)
ax[1].set_title('Mask')
ax[1].imshow(mask, cmap=plt.cm.gray)
ax[2].set_title('Defected image')
ax[2].imshow(image_defect)
ax[3].set_title('Inpainted image')
ax[3].imshow(image_result)
for a in ax:
a.axis('off')
fig.tight_layout()
plt.show()
```

**Total running time of the script:** ( 0 minutes 6.964 seconds)