Image compression via tensor decomposition

Example on how to use tensorly.decomposition.parafac`and :func:`tensorly.decomposition.tucker on images.

../../_images/sphx_glr_plot_image_compression_001.png
import matplotlib.pyplot as plt
import tensorly as tl
import numpy as np
from scipy.misc import face, imresize
from tensorly.decomposition import parafac
from tensorly.decomposition import tucker
from math import ceil


random_state = 12345

image = tl.tensor(imresize(face(), 0.3), dtype='float64')

def to_image(tensor):
    """A convenience function to convert from a float dtype back to uint8"""
    im = tl.to_numpy(tensor)
    im -= im.min()
    im /= im.max()
    im *= 255
    return im.astype(np.uint8)

# Rank of the CP decomposition
cp_rank = 25
# Rank of the Tucker decomposition
tucker_rank = [100, 100, 2]

# Perform the CP decomposition
factors = parafac(image, rank=cp_rank, init='random', tol=10e-6)
# Reconstruct the image from the factors
cp_reconstruction = tl.kruskal_to_tensor(factors)

# Tucker decomposition
core, tucker_factors = tucker(image, ranks=tucker_rank, init='random', tol=10e-5, random_state=random_state)
tucker_reconstruction = tl.tucker_to_tensor(core, tucker_factors)

# Plotting the original and reconstruction from the decompositions
fig = plt.figure()
ax = fig.add_subplot(1, 3, 1)
ax.set_axis_off()
ax.imshow(to_image(image))
ax.set_title('original')

ax = fig.add_subplot(1, 3, 2)
ax.set_axis_off()
ax.imshow(to_image(cp_reconstruction))
ax.set_title('CP')

ax = fig.add_subplot(1, 3, 3)
ax.set_axis_off()
ax.imshow(to_image(tucker_reconstruction))
ax.set_title('Tucker')

plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 1.919 seconds)

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