Rename deconvolution to restoration

doc/examples/plot_restoration.py

@@ -30,7 +30,7 @@ data learning. This is not common and based on the following publication
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage import color, data, deconvolution
+from skimage import color, data, restoration
 
 lena = color.rgb2gray(data.lena())
 from scipy.signal import convolve2d as conv2
@@ -38,7 +38,7 @@ psf = np.ones((5, 5)) / 25
 lena = conv2(lena, psf, 'same')
 lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
 
-deconvolued, _ = deconvolution.unsupervised_wiener(lena, psf)
+deconvolued, _ = restoration.unsupervised_wiener(lena, psf)
 
 fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
 
@@ -50,7 +50,7 @@ ax[0].set_title('Data')
 
 ax[1].imshow(deconvolued, vmax=lena.max())
 ax[1].axis('off')
-ax[1].set_title('Self tuned deconvolution')
+ax[1].set_title('Self tuned restoration')
 
 fig.subplots_adjust(wspace=0.02, hspace=0.2,
                     top=0.9, bottom=0.05, left=0, right=1)

skimage/restoration/__init__.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
-"""Skimage module for image deconvolution
+"""Skimage module for image restoration
 
 This module implement various algorithm of the literature for image
-deconvolution.
+restoration.
 
 References
 ----------

skimage/restoration/tests/test_deconvolution.py

@@ -5,7 +5,7 @@ from scipy.signal import convolve2d
 
 import skimage
 from skimage.data import camera
-from skimage import deconvolution
+from skimage import restoration
 
 test_img = skimage.img_as_float(camera())
 
@@ -15,7 +15,7 @@ def test_wiener():
     data = convolve2d(test_img, psf, 'same')
     np.random.seed(0)
     data += 0.1 * data.std() * np.random.standard_normal(data.shape)
-    deconvolved = deconvolution.wiener(data, psf, 0.05)
+    deconvolved = restoration.wiener(data, psf, 0.05)
 
     path = pjoin(dirname(abspath(__file__)), 'camera_wiener.npy')
     np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
@@ -26,7 +26,7 @@ def test_unsupervised_wiener():
     data = convolve2d(test_img, psf, 'same')
     np.random.seed(0)
     data += 0.1 * data.std() * np.random.standard_normal(data.shape)
-    deconvolved, _ = deconvolution.unsupervised_wiener(data, psf)
+    deconvolved, _ = restoration.unsupervised_wiener(data, psf)
 
     path = pjoin(dirname(abspath(__file__)), 'camera_unsup.npy')
     np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
@@ -37,7 +37,7 @@ def test_richardson_lucy():
     data = convolve2d(test_img, psf, 'same')
     np.random.seed(0)
     data += 0.1 * data.std() * np.random.standard_normal(data.shape)
-    deconvolved = deconvolution.richardson_lucy(data, psf, 5)
+    deconvolved = restoration.richardson_lucy(data, psf, 5)
 
     path = pjoin(dirname(abspath(__file__)), 'camera_rl.npy')
     np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)

skimage/restoration/uft.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 # uft.py --- Unitary fourier transform
 
-# Copyright (c) 2011, 2012, 2013  Fran��ois Orieux <orieux@iap.fr>
+# Copyright (c) 2011, 2012, 2013  François Orieux <orieux@iap.fr>
 
 # Permission is hereby granted, free of charge, to any person
 # obtaining a copy of this software and associated documentation files