Example generation from scripts, taken from scikit learn

(ext/gen_rst.py)

examples/plot_lena_tv_denoise.py

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+"""
+====================================================
+Denoising the picture of Lena using total variation
+====================================================
+
+In this example, we denoise a noisy version of the picture of Lena using the
+total variation denoising filter. The result of this filter is an image that
+has a minimal total variation norm, while being as close to the initial image
+as possible. The total variation is the L1 norm of the gradient of the image,
+and minimizing the total variation typically produces "posterized" images with
+flat domains separated by sharp edges.
+
+It is possible to change the degree of posterization by controlling the
+tradeoff between denoising and faithfulness to the original image.
+
+"""
+
+import numpy as np
+import scipy
+from scipy import ndimage
+import matplotlib.pyplot as plt
+from scikits.image.filter import tv_denoise
+
+l = scipy.lena()
+l = l[230:290, 220:320]
+
+noisy = l + 0.4*l.std()*np.random.random(l.shape)
+
+tv_denoised = tv_denoise(noisy, weight=10)
+
+
+plt.figure(figsize=(12,2.8))
+
+plt.subplot(131)
+plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('noisy', fontsize=20)
+plt.subplot(132)
+plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('TV denoising', fontsize=20)
+
+tv_denoised = tv_denoise(noisy, weight=50)
+plt.subplot(133)
+plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('(more) TV denoising', fontsize=20)
+
+plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, 
+                                            right=1)
+