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  3. Image Processing in Python

Exercise

Let's restore a damaged image

In this exercise, we'll restore an image that has missing parts in it, using the inpaint_biharmonic() function.

Small cute puppy
Loaded as defect_image.

We'll work on an image from the data module, obtained by data.astronaut(). Some of the pixels have been replaced with 0s using a binary mask, on purpose, to simulate a damaged image. Replacing pixels with 0s turns them totally black. The defective image is saved as an array called defect_image.

The mask is a black and white image with patches that have the position of the image bits that have been corrupted. We can apply the restoration function on these areas. This mask is preloaded as mask.

Remember that inpainting is the process of reconstructing lost or deteriorated parts of images and videos.

Instructions 1/3

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  • Import the inpaint function in the restoration module in scikit-image (skimage).