Inpainting is the process of restoring damaged or missing parts of an image. Suppose we have a binary mask, D, that specifies the location of the damaged pixels in the input image, f, as shown here:
Once the damaged regions in the image are located with the mask, the lost/damaged pixels have to be reconstructed with some algorithm (for example, Total Variation Inpainting). The reconstruction is supposed to be performed fully automatically by exploiting the information presented in non-damaged regions.
In this example, we shall demonstrate an image inpainting implementation with scikit-imagerestoration module's inpaint_biharmonic() function. Let's apply a mask to create a damaged image from the original Lena colored image. The following code block shows how the masked pixels in the damaged image get inpainted by the inpainting algorithm based on a biharmonic equation assumption:
import numpy as np import matplotlib.pyplot as pylab from skimage.io import imread, imsave from...
