From 0fdb5b2e0a20f983f619f1fd2f22830070ff147c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Orieux?= Date: Thu, 5 Dec 2013 11:56:59 +0100 Subject: [PATCH] Change example scaling. Fix ref in doc. --- doc/examples/plot_restoration.py | 4 ++-- skimage/restoration/deconvolution.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/examples/plot_restoration.py b/doc/examples/plot_restoration.py index 793377e9..fdffd953 100644 --- a/doc/examples/plot_restoration.py +++ b/doc/examples/plot_restoration.py @@ -46,11 +46,11 @@ fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5)) plt.gray() -ax[0].imshow(lena) +ax[0].imshow(lena, vmin=deconvolved.min(), vmax=deconvolved.max()) ax[0].axis('off') ax[0].set_title('Data') -ax[1].imshow(deconvolved, vmax=lena.max()) +ax[1].imshow(deconvolved) ax[1].axis('off') ax[1].set_title('Self tuned restoration') diff --git a/skimage/restoration/deconvolution.py b/skimage/restoration/deconvolution.py index 5d3bd55a..367af563 100644 --- a/skimage/restoration/deconvolution.py +++ b/skimage/restoration/deconvolution.py @@ -153,8 +153,8 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None): Return the deconvolution with a Wiener-Hunt approach, where the hyperparameters are automatically estimated. The algorithm is a - stochastic iterative process (Gibbs sampler) described in [1]. See - also `wiener` function. + stochastic iterative process (Gibbs sampler) described in ref + belo]. See also `wiener` function. Parameters ----------