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Olivier Debeir
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
"""
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====================================================
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Denoising the picture of Lena using bilateral filter
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====================================================
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In this example, we denoise a noisy version of the picture of Lena
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using an approximation of a bilateral filter.
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The pixels used to compute a local mean respect these conditions:
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- be close to the central pixel, i.e. belong to the given structuring element.
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- have a similar gray level, similarity is fixed by an interval [-s0,+s1] centered on the central pixel gray level.
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The filter used is an approximation of a classical bilateral filter in the sens that kernel are usually gaussian
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both in spatial and spectral dimensions.
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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_ubyte
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from skimage.rank import bilateral_mean
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from skimage.morphology import disk
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l = img_as_ubyte(color.rgb2gray(data.lena()))
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l = l[230:290, 220:320]
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noisy = l + 0.4 * l.std() * np.random.random(l.shape)
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selem = disk(30)
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bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10)
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plt.figure(figsize=(8, 2))
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plt.subplot(131)
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plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)
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plt.axis('off')
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plt.title('noisy', fontsize=20)
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plt.subplot(132)
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plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
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plt.axis('off')
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plt.title('bilateral denoising', fontsize=20)
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selem = disk(30)
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bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40)
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plt.subplot(133)
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plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
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plt.axis('off')
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plt.title('(more) bilateral denoising', fontsize=20)
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plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,right=1)
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plt.show()
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