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Tony S Yu
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
"""
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====================================================
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Denoising the picture of Lena using total variation
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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 the total variation denoising filter. The result of this filter
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is an image that has a minimal total variation norm, while being as
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close to the initial image as possible. The total variation is the L1
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norm of the gradient of the image, and minimizing the total variation
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typically produces "posterized" images with flat domains separated by
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sharp edges.
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It is possible to change the degree of posterization by controlling
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the tradeoff between denoising and faithfulness to the original image.
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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.filter import tv_denoise
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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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tv_denoised = tv_denoise(noisy, weight=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(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
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plt.axis('off')
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plt.title('TV denoising', fontsize=20)
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tv_denoised = tv_denoise(noisy, weight=50)
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plt.subplot(133)
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plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
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plt.axis('off')
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plt.title('(more) TV denoising', fontsize=20)
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plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,
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right=1)
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plt.show()
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