mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-04 02:45:22 +08:00
Emmanuelle Gouillart
a5ed4acf86
- denoising RGB is now possible, and "colored patches" are then compared - the main function is now in a pure Python file so that default values of kw arguments are visible in the help - reduced the number of computations of patches bound (but this doesn't change much the total speed). - added an example for the gallery I also played with functions that could replace the exponential by a faster and less precise function, but it turns out that most of the time is spent in additions and multiplications when computing the distance between two patches.
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 Lena 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.filter import nl_means_denoising
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lena = img_as_float(data.lena())
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lena = lena[200:300, 100:200]
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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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denoise = nl_means_denoising(noisy, 7, 9, 0.06)
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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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