From 8de3ec988de42d9ceb92e6d436f7d332eb5d337f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= <hannesschoenberger@gmail.com>
Date: Sat, 6 Oct 2012 22:51:16 +0200
Subject: [PATCH] Add example script for bilateral denoising

---
 doc/examples/plot_denoise.py         | 64 ++++++++++++++++++++++++++++
 doc/examples/plot_lena_tv_denoise.py | 51 ----------------------
 2 files changed, 64 insertions(+), 51 deletions(-)
 create mode 100644 doc/examples/plot_denoise.py
 delete mode 100644 doc/examples/plot_lena_tv_denoise.py

diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py
new file mode 100644
index 00000000..30dfc10c
--- /dev/null
+++ b/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()
diff --git a/doc/examples/plot_lena_tv_denoise.py b/doc/examples/plot_lena_tv_denoise.py
deleted file mode 100644
index bce07845..00000000
--- a/doc/examples/plot_lena_tv_denoise.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""
-====================================================
-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
-
-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)
-
-tv_denoised = tv_denoise(noisy, weight=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(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)
-plt.show()