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Improve doc string format of TV denoise functions

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This commit is contained in:
Johannes Schönberger
2012-10-17 09:11:34 +02:00
parent b0807739ef
commit 25867b9dd5
1 changed files with 22 additions and 33 deletions
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skimage/filter/denoise.py
+22 -33
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@@ -3,35 +3,32 @@ from skimage import img_as_float
def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
"""
Perform total-variation denoising on 3-D arrays
"""Perform total-variation denoising on 3-D arrays.
Parameters
----------
im: ndarray
3-D input data to be denoised
3-D input data to be denoised.
weight: float, optional
denoising weight. The greater ``weight``, the more denoising (at
the expense of fidelity to ``input``)
Denoising weight. The greater ``weight``, the more denoising (at
the expense of fidelity to ``input``).
eps: float, optional
relative difference of the value of the cost function that determines
Relative difference of the value of the cost function that determines
the stop criterion. The algorithm stops when:
(E_(n-1) - E_n) < eps * E_0
n_iter_max: int, optional
maximal number of iterations used for the optimization.
Maximal number of iterations used for the optimization.
Returns
-------
out: ndarray
denoised array of floats
Denoised array of floats.
Notes
-----
Rudin, Osher and Fatemi algorithm
Rudin, Osher and Fatemi algorithm.
Examples
---------
@@ -86,43 +83,39 @@ def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
"""
Perform total-variation denoising
"""Perform total-variation denoising.
Parameters
----------
im: ndarray
input data to be denoised
Input data to be denoised.
weight: float, optional
denoising weight. The greater ``weight``, the more denoising (at
Denoising weight. The greater ``weight``, the more denoising (at
the expense of fidelity to ``input``)
eps: float, optional
relative difference of the value of the cost function that determines
Relative difference of the value of the cost function that determines
the stop criterion. The algorithm stops when:
(E_(n-1) - E_n) < eps * E_0
n_iter_max: int, optional
maximal number of iterations used for the optimization.
Maximal number of iterations used for the optimization.
Returns
-------
out: ndarray
denoised array of floats
Denoised array of floats.
Notes
-----
The principle of total variation denoising is explained in
http://en.wikipedia.org/wiki/Total_variation_denoising
http://en.wikipedia.org/wiki/Total_variation_denoising.
This code is an implementation of the algorithm of Rudin, Fatemi and Osher
that was proposed by Chambolle in [1]_.
References
----------
.. [1] A. Chambolle, An algorithm for total variation minimization and
applications, Journal of Mathematical Imaging and Vision,
Springer, 2004, 20, 89-97.
@@ -173,33 +166,30 @@ def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200):
"""
Perform total-variation denoising on 2-d and 3-d images
"""Perform total-variation denoising on 2-d and 3-d images.
Parameters
----------
im: ndarray (2d or 3d) of ints, uints or floats
input data to be denoised. `im` can be of any numeric type,
Input data to be denoised. `im` can be of any numeric type,
but it is cast into an ndarray of floats for the computation
of the denoised image.
weight: float, optional
denoising weight. The greater ``weight``, the more denoising (at
the expense of fidelity to ``input``)
Denoising weight. The greater ``weight``, the more denoising (at
the expense of fidelity to ``input``).
eps: float, optional
relative difference of the value of the cost function that
Relative difference of the value of the cost function that
determines the stop criterion. The algorithm stops when:
(E_(n-1) - E_n) < eps * E_0
n_iter_max: int, optional
maximal number of iterations used for the optimization.
Maximal number of iterations used for the optimization.
Returns
-------
out: ndarray
denoised array of floats
Denoised array of floats.
Notes
-----
@@ -217,7 +207,6 @@ def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200):
References
----------
.. [1] A. Chambolle, An algorithm for total variation minimization and
applications, Journal of Mathematical Imaging and Vision,
Springer, 2004, 20, 89-97.