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.
Wikipedia. Inpainting https://en.wikipedia.org/wiki/Inpainting
Wikipedia. Biharmonic equation https://en.wikipedia.org/wiki/Biharmonic_equation
N.S.Hoang, S.B.Damelin, “On surface completion and image inpainting by biharmonic functions: numerical aspects”, arXiv:1707.06567
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.set_title('Original image') ax.imshow(image_orig) ax.set_title('Mask') ax.imshow(mask, cmap=plt.cm.gray) ax.set_title('Defected image') ax.imshow(image_defect) ax.set_title('Inpainted image') ax.imshow(image_result) for a in ax: a.axis('off') fig.tight_layout() plt.show()
Total running time of the script: ( 0 minutes 5.475 seconds)