Merge pull request #1804 from soupault/inpainting

ENH: Inpainting with biharmonic equation

doc/examples/filters/plot_inpaint.py

@@ -0,0 +1,58 @@
+"""
+===========
+Inpainting
+===========
+Inpainting [1]_ is the process of reconstructing lost or deteriorated
+parts of images and videos. 
+
+The reconstruction is supposed to be performed in fully automatic way by
+exploiting the information presented in non-damaged regions.
+
+In this example, we show how the masked pixels get inpainted by
+inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_.
+
+.. [1]  Wikipedia. Inpainting
+        https://en.wikipedia.org/wiki/Inpainting
+.. [2]  Wikipedia. Biharmonic equation
+        https://en.wikipedia.org/wiki/Biharmonic_equation
+.. [3]  N.S.Hoang, S.B.Damelin, "On surface completion and image 
+        inpainting by biharmonic functions: numerical aspects",
+        http://www.ima.umn.edu/~damelin/biharmonic
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data, color
+from skimage.restoration import inpaint
+
+image_orig = data.astronaut()
+
+# Create mask with three defect regions: left, middle, right respectively
+mask = np.zeros(image_orig.shape[:-1])
+mask[20:60, 0:20] = 1
+mask[200:300, 150:170] = 1
+mask[50:100, 400:430] = 1
+
+# Defect image over the same region in each color channel
+image_defect = image_orig.copy()
+for layer in range(image_defect.shape[-1]):
+    image_defect[np.where(mask)] = 0
+
+image_result = inpaint.inpaint_biharmonic(image_defect, mask, multichannel=True)
+
+fig, axes = plt.subplots(ncols=3, nrows=1)
+
+axes[0].set_title('Defected image')
+axes[0].imshow(image_orig)
+axes[0].set_xticks([]), axes[0].set_yticks([])
+
+axes[1].set_title('Defect mask')
+axes[1].imshow(mask, cmap=plt.cm.gray)
+axes[1].set_xticks([]), axes[1].set_yticks([])
+
+axes[2].set_title('Inpainted image')
+axes[2].imshow(image_result)
+axes[2].set_xticks([]), axes[2].set_yticks([])
+
+plt.show()