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Interpolation

Interpolation is how new pixel intensities are estimated when an image transformation is applied. It is implemented in SciPy using sets of spline functions.

Editing the interpolation order when using a function such as ndi.zoom() modifies the resulting estimate: higher orders provide more flexible estimates but take longer to compute.

For this exercise, upsample im and investigate the effect of different interpolation orders on the resulting image.

Cet exercice fait partie du cours

Biomedical Image Analysis in Python

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Instructions

  • Use ndi.zoom() to upsample im from a shape of 128, 128 to 512, 512 twice. First, use an interpolation order of 0, then set order to 5.
  • Print the array shapes of im and up0.
  • Plot close-ups of the images. Use the index range 128:256 along each axis.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Upsample "im" by a factor of 4
up0 = ndi.zoom(____, zoom=____, order=____)
up5 = ____

# Print original and new shape
print('Original shape:', ____)
print('Upsampled shape:', ____)

# Plot close-ups of the new images
fig, axes = plt.subplots(1, 2)
axes[0].imshow(up0[128:256, 128:256])
axes[1].imshow(____)
format_and_render_plots()
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