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emmanuelle
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
"""
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=================================================
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Non-local means denoising for preserving textures
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=================================================
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In this example, we denoise a detail of the astronaut image using the non-local
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means filter. The non-local means algorithm replaces the value of a pixel by an
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average of a selection of other pixels values: small patches centered on the
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other pixels are compared to the patch centered on the pixel of interest, and
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the average is performed only for pixels that have patches close to the current
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patch. As a result, this algorithm can restore well textures, that would be
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blurred by other denoising algoritm.
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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, img_as_float
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from skimage.restoration import nl_means_denoising
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astro = img_as_float(data.astronaut())
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astro = astro[30:180, 150:300]
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noisy = astro + 0.3 * np.random.random(astro.shape)
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noisy = np.clip(noisy, 0, 1)
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denoise = nl_means_denoising(noisy, 7, 9, 0.08)
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fig, ax = plt.subplots(ncols=2, figsize=(8, 4))
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ax[0].imshow(noisy)
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ax[0].axis('off')
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ax[0].set_title('noisy')
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ax[1].imshow(denoise)
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ax[1].axis('off')
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ax[1].set_title('non-local means')
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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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