mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
"""
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=============================
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Denoising the picture of Lena
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=============================
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In this example, we denoise a noisy version of the picture of Lena using the
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total variation and bilateral denoising filter.
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These algorithms typically produce "posterized" images with flat domains
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separated by sharp edges. It is possible to change the degree of posterization
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by controlling the tradeoff between denoising and faithfulness to the original
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image.
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Total variation filter
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----------------------
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The result of this filter is an image that has a minimal total variation norm,
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while being as close to the initial image as possible. The total variation is
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the L1 norm of the gradient of the image.
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Bilateral filter
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----------------
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A bilateral filter is an edge-preserving and noise reducing filter. It averages
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pixels based on their spatial closeness and radiometric similarity.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import data, color, img_as_float
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from skimage.filter import denoise_tv_chambolle, denoise_bilateral
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lena = img_as_float(data.lena())
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lena = lena[220:300, 220:320]
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noisy = lena + 0.6 * lena.std() * np.random.random(lena.shape)
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noisy = np.clip(noisy, 0, 1)
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fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
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plt.gray()
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ax[0, 0].imshow(noisy)
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ax[0, 0].axis('off')
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ax[0, 0].set_title('noisy')
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ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True))
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ax[0, 1].axis('off')
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ax[0, 1].set_title('TV')
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ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15))
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ax[0, 2].axis('off')
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ax[0, 2].set_title('Bilateral')
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ax[1, 0].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True))
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ax[1, 0].axis('off')
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ax[1, 0].set_title('(more) TV')
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ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15))
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ax[1, 1].axis('off')
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ax[1, 1].set_title('(more) Bilateral')
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ax[1, 2].imshow(lena)
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ax[1, 2].axis('off')
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ax[1, 2].set_title('original')
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fig.subplots_adjust(wspace=0.02, hspace=0.2,
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top=0.9, bottom=0.05, left=0, right=1)
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plt.show()
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