add examples - to be cont.

doc/examples/plot_lena_bilateral_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 matplotlib.pyplot as plt
+
+from skimage import data, color, img_as_ubyte
+from skimage.filter import tv_denoise
+from skimage.rank import bilateral_mean
+from skimage.morphology import disk
+
+l = img_as_ubyte(color.rgb2gray(data.lena()))
+l = l[230:290, 220:320]
+
+noisy = l + 0.4 * l.std() * np.random.random(l.shape)
+
+selem = disk(30)
+bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10)
+
+plt.figure(figsize=(8, 2))
+
+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(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('bilateral denoising', fontsize=20)
+
+selem = disk(30)
+bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30)
+plt.subplot(133)
+plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('(more) bilateral denoising', fontsize=20)
+
+plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,
+                    right=1)
+plt.show()