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https://github.com/wassname/scikit-image.git
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Some improvements to new deconvolution code
This commit is contained in:
Christoph Deil
committed by
François Orieux
François Orieux
parent
17cc22802c
commit
e25ccf61f6
@@ -1,6 +1,7 @@
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# -*- coding: utf-8 -*-
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"""Skimage module for image deconvolution
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This module implement various algorithm of the literarture for image
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This module implement various algorithm of the literature for image
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deconvolution.
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References
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@@ -1,38 +1,33 @@
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import warnings
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from os.path import abspath, dirname, join as pjoin
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import numpy as np
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import numpy.testing.assert_array_almost_equal
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from scipy.signal import convolve2d
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from skimage.data import camera
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from skimage import deconvolution
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from scipy.signal import convolve2d as conv2
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from skimage import data, deconvolution
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# Test deconvolution
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# ===========================
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test_img = data.camera().astype(np.float)
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test_img = camera().astype(np.float)
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def test_wiener():
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psf = np.ones((5, 5))
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data = conv2(test_img, psf, 'same')
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data = convolve2d(test_img, psf, 'same')
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np.random.seed(0)
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data += 0.1 * data.std() * np.random.standard_normal(data.shape)
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deconvolued = deconvolution.wiener(data, psf, 25)
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numpy.testing.assert_array_almost_equal(deconvolued,
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np.load("./camera_wiener.npy"))
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path = pjoin(dirname(abspath(__file__)), 'camera_wiener.npy')
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np.testing.assert_array_almost_equal(deconvolued, np.load(path))
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def test_unsupervised_wiener():
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psf = np.ones((5, 5))
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data = conv2(test_img, psf, 'same')
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data = convolve2d(test_img, psf, 'same')
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np.random.seed(0)
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data += 0.1 * data.std() * np.random.standard_normal(data.shape)
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deconvolued, _ = deconvolution.unsupervised_wiener(data, psf)
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numpy.testing.assert_array_almost_equal(deconvolued,
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np.load("./camera_unsup.npy"))
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path = pjoin(dirname(abspath(__file__)), 'camera_unsup.npy')
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np.testing.assert_array_almost_equal(deconvolued, np.load(path))
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def test_rychardson_lucy():
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def test_richardson_lucy():
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return True
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@@ -1,7 +1,7 @@
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# -*- coding: utf-8 -*-
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# uft.py --- Unitary fourier transform
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# Copyright (c) 2011, 2012, 2013 François Orieux <orieux@iap.fr>
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# Copyright (c) 2011, 2012, 2013 Fran��ois Orieux <orieux@iap.fr>
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# Permission is hereby granted, free of charge, to any person
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# obtaining a copy of this software and associated documentation files
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@@ -63,12 +63,12 @@ except ImportError:
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" by using fftw library.")
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ANFFTMOD = False
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__author__ = "François Orieux"
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__author__ = "Fran��ois Orieux"
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__copyright__ = "Copyright (C) 2011, 2012, 2013 F. Orieux <orieux@iap.fr>"
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__credits__ = ["François Orieux"]
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__credits__ = ["Fran��ois Orieux"]
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__license__ = "mit"
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__version__ = "0.1.0"
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__maintainer__ = "François Orieux"
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__maintainer__ = "Fran��ois Orieux"
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__email__ = "orieux@iap.fr"
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__status__ = "development"
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__url__ = ""
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@@ -427,12 +427,12 @@ def ir2tf(imp_resp, shape, dim=None, real=True):
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for i, s in enumerate(imp_resp.shape)])
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if real:
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if anfft:
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if ANFFTMOD:
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return anfft.rfftn(irpadded, k=dim)
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else:
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return np.fft.rfftn(irpadded, axes=range(-dim, 0))
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else:
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if anfft:
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if ANFFTMOD:
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return anfft.fftn(irpadded, k=dim)
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else:
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return np.fft.fftn(irpadded, axes=range(-dim, 0))
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@@ -22,12 +22,8 @@
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# Commentary:
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"""Implementations deconvolution functions"""
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# code:
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from __future__ import division
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import numpy as np
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@@ -81,15 +77,16 @@ def wiener(data, psf, reg_val, reg=None, real=True):
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im_deconv : (M, N) ndarray
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The deconvolued data
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Exemples
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Examples
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--------
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>>> import numpy as np
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>>> from skimage import color, data, deconvolution
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>>> lena = color.rgb2gray(data.lena())
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>>> from scipy.signal import convolve2d as conv2
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>>> from scipy.signal import convolve2d
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>>> psf = np.ones((5, 5))
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>>> lena = conv2(lena, psf, 'same')
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>>> lena = convolve2d(lena, psf, 'same')
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>>> lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
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>>> deconvolued_lena = deconvolution.wiener(lena, psf, 1100)
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>>> deconvolved_lena = deconvolution.wiener(lena, psf, 1100)
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References
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----------
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@@ -184,15 +181,16 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
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exists, the current image sample. This function can be used to
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store the sample, or compute other moments than the mean.
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Exemples
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Examples
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--------
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>>> import numpy as np
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>>> from skimage import color, data, deconvolution
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>>> lena = color.rgb2gray(data.lena())
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>>> from scipy.signal import convolve2d as conv2
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>>> from scipy.signal import convolve2d
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>>> psf = np.ones((5, 5))
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>>> lena = conv2(lena, psf, 'same')
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>>> lena = convolve2d(lena, psf, 'same')
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>>> lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
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>>> deconvolued_lena = deconvolution.unsupervised_wiener(lena, psf)
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>>> deconvolved_lena = deconvolution.unsupervised_wiener(lena, psf)
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References
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----------
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@@ -201,7 +199,7 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
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spread function parameters for Wiener-Hunt deconvolution",
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J. Opt. Soc. Am. A 27, 1593-1607 (2010)
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http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593
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http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593
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"""
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params = {'threshold': 1e-4, 'max_iter': 200,
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'min_iter': 30, 'burnin': 15, 'callback': None}
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@@ -285,11 +283,8 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
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def richardson_lucy(data, psf, iterations=50):
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"""Richardson lucy deconvolution
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"""Richardson-Lucy deconvolution.
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This a python conversion of the wikipedia page on this algorithm. See
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http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution
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Parameters
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----------
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@@ -297,28 +292,26 @@ def richardson_lucy(data, psf, iterations=50):
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The data
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psf : ndarray
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The impulsionnal response in real space.
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The point spread function
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iterations : int
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dictionary of gibbs parameters. See below.
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Number of iterations
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Returns
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-------
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im_deconv : ndarray
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The deconvolued image
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The deconvolved image
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References
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----------
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.. [2] Richardson, William Hadley, "Bayesian-Based Iterative
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Method of Image Restoration". JOSA 62 (1):
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55–59. doi:10.1364/JOSA.62.000055, 1972
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.. [2] http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution
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"""
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data = data.astype(np.float)
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psf = psf.astype(np.float)
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im_deconv = 0.5 * np.ones(data.shape)
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psf_mirror = psf[::-1, ::-1]
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for iteration in range(iterations):
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for _ in range(iterations):
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relative_blur = data / conv2(im_deconv, psf, 'same')
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im_deconv *= conv2(relative_blur, psf_mirror, 'same')
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