Add deconvolution module to skimage.

This module add three function to skimage. The `wiener` function is a
simple wiener deconvolution. The `unsupervised_wiener` is a more
sophisticated wiener deconvolution with automatic estimation of
regularisation parameters. The third function is a literal traduction
in python of the rychardson lucy deconvolution of wikipedia.

doc/examples/plot_deconvolution.py

@@ -0,0 +1,58 @@
+# -*- coding: utf-8 -*-
+"""
+=====================
+Deconvolution of Lena
+=====================
+
+In this example, we deconvolve a noisy version of Lena using wiener
+and unsupervised wiener algorithms. This algorithms are known to not
+have the best respect of sharp edge in the image.
+
+Wiener filter
+-------------
+
+This is simply the inverse filter based on the PSF, the prior
+regularisation (penalisation of high frequency) and the tradeoff
+between the data and prior adequacy. The regularization parameter must
+be hand tuned.
+
+Unsupervised wiener
+-------------------
+
+This algorithm has a self tuned regularisation parameters based on
+data learning. This is not common and based on the following publication
+
+.. [1] François Orieux, Jean-François Giovannelli, and Thomas
+       Rodet, "Bayesian estimation of regularization and point
+       spread function parameters for Wiener-Hunt deconvolution",
+       J. Opt. Soc. Am. A 27, 1593-1607 (2010)
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import color, data, deconvolution
+
+lena = color.rgb2gray(data.lena())
+from scipy.signal import convolve2d as conv2
+psf = np.ones((5, 5))
+lena = conv2(lena, psf, 'same')
+lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
+
+deconvolued, _ = deconvolution.unsupervised_wiener(lena, psf)
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
+
+plt.gray()
+
+ax[0].imshow(lena)
+ax[0].axis('off')
+ax[0].set_title('Data')
+
+ax[1].imshow(deconvolued)
+ax[1].axis('off')
+ax[1].set_title('Deconvolution')
+
+fig.subplots_adjust(wspace=0.02, hspace=0.2,
+                    top=0.9, bottom=0.05, left=0, right=1)
+
+plt.show()