Add example script for bilateral denoising

doc/examples/plot_denoise.py

@@ -0,0 +1,64 @@
+"""
+=============================
+Denoising the picture of Lena
+=============================
+
+In this example, we denoise a noisy version of the picture of Lena using the
+total variation and bilateral denoising filter.
+
+These algorithms typically produce "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.
+
+Total variation 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.
+
+Bilateral filter
+----------------
+A bilateral filter is an edge-preserving and noise reducing denoising filter.
+It averages pixel based on their spatial closeness and radiometric similarity.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data, color, img_as_float
+from skimage.filter import tv_denoise, denoise_bilateral
+
+lena = img_as_float(data.lena())
+lena = lena[220:300, 220:320]
+
+noisy = lena + 0.5 * lena.std() * np.random.random(lena.shape)
+noisy = np.clip(noisy, 0, 1)
+
+fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
+
+ax[0, 0].imshow(lena)
+ax[0, 0].axis('off')
+ax[0, 0].set_title('original')
+ax[0, 1].imshow(tv_denoise(noisy, weight=0.02))
+ax[0, 1].axis('off')
+ax[0, 1].set_title('TV')
+ax[0, 2].imshow(tv_denoise(noisy, weight=0.05))
+ax[0, 2].axis('off')
+ax[0, 2].set_title('(more) TV')
+
+ax[1, 0].imshow(noisy)
+ax[1, 0].axis('off')
+ax[1, 0].set_title('original')
+ax[1, 1].imshow(denoise_bilateral(noisy, sigma_color=0.02, sigma_range=15))
+ax[1, 1].axis('off')
+ax[1, 1].set_title('Bilateral')
+ax[1, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_range=15))
+ax[1, 2].axis('off')
+ax[1, 2].set_title('(more) Bilateral')
+
+fig.subplots_adjust(wspace=0.02, hspace=0.2, top=0.9, bottom=0.05, left=0, right=1)
+
+plt.show()