crikit.preprocess.algorithms package¶
Submodules¶
crikit.preprocess.algorithms.als module¶
Created on Mon Dec 5 12:12:51 2016
@author: chc
-
class
crikit.preprocess.algorithms.als.
AlsCvxopt
(smoothness_param=1000.0, asym_param=0.0001, redux=1, order=2, rng=None, fix_end_points=False, fix_rng=None, fix_const=1, max_iter=100, min_diff=1e-05, verbose=False, use_prev=True, **kwargs)[source]¶ Bases:
crikit.preprocess.algorithms.abstract_als.AbstractBaseline
-
property
asym_param
¶
-
property
crikit.preprocess.algorithms.anscombe module¶
Variance Stabilization
- Routines:
- gen_anscombe_forward
Generalized forward Anscombe transformation
- gen_anscombe_inverse_closed_form
Closed-form approximation of the exact unbiased inverse of Generalized Anscombe variance-stabilizing transformation
- gen_anscombe_exact_unbiased
Exact unbiased inverse of Generalized Anscombe variance-stabilizing
Notes
This software is a direct translation (with minor alterations) of the original MATLAB software created by Alessandro Foi and Markku Mäkitalo (Tampere University of Technology - 2011-2012). Please cite the references below if using this software. http://www.cs.tut.fi/~foi/
References
- [1] M. Mäkitalo and A. Foi, “Optimal inversion of the generalized Anscombe
transformation for Poisson-Gaussian noise”, IEEE Trans. Image Process., doi:10.1109/TIP.2012.2202675
- [2] J.L. Starck, F. Murtagh, and A. Bijaoui, Image Processing and Data
Analysis, Cambridge University Press, Cambridge, 1998)
-
crikit.preprocess.algorithms.anscombe.
anscombe_inverse_exact_unbiased
(fsignal)[source]¶ Applies an exact, unbiased inverse of the Anscombe variance-stabilizing transformation assuming a mixed Poisson-Gaussian noise model as:
signal = poisson_multi*Poisson{signal0} + Gauss{gauss_mean, gauss_std},
where Poisson{} and Gauss{} are generalized descriptions of Poisson and Gaussian noise.
- Parameters
fsignal (ndarray) – Forward Anscombe-transformed noisy signal (1-,2-,3D)
- Returns
signal – Inverse Anscombe-transformed signal
- Return type
ndarray (matched to signal shape)
Notes
This software is a direct translation (with minor alterations) of the original MATLAB software created by Alessandro Foi and Markku Mäkitalo (Tampere University of Technology - 2011-2012). Please cite the references below if using this software. http://www.cs.tut.fi/~foi/
References
- [1] M. Mäkitalo and A. Foi, “On the inversion of the Anscombe
transformation in low-count Poisson image denoising”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2009, Tuusula, Finland, pp. 26-32, August 2009. doi:10.1109/LNLA.2009.5278406
- [2] M. Mäkitalo and A. Foi, “Optimal inversion of the Anscombe
transformation in low-count Poisson image denoising”, IEEE Trans. Image Process., vol. 20, no. 1, pp. 99-109, January 2011. doi:10.1109/TIP.2010.2056693
- [3] Anscombe, F.J., “The transformation of Poisson, binomial and
negative-binomial data”, Biometrika, vol. 35, no. 3/4, pp. 246-254, Dec. 1948.
-
crikit.preprocess.algorithms.anscombe.
gen_anscombe_forward
(signal, gauss_std, gauss_mean=0, poisson_multi=1)[source]¶ Applies the generalized Anscombe variance-stabilization transform assuming a mixed Poisson-Gaussian noise model as:
signal = poisson_multi*Poisson{signal0} + Gauss{gauss_mean, gauss_std},
where Poisson{} and Gauss{} are generalized descriptions of Poisson and Gaussian noise.
- Parameters
signal (ndarray) – Noisy signal (1-,2-,3D)
gauss_std (float, int) – Standard deviation of Gaussian noise
poisson_multi (float or int, optional (default = 1)) – Effectively a multiplier that scales the effect of the Poisson noise
gauss_mean (float or int, optional (default = 0)) – Mean Gaussian noise level
- Returns
fsignal – “Anscombe-transformed” signal with an approximate unity standard deviation/variance (~ 1)
- Return type
ndarray (matched to signal shape)
Notes
This software is a direct translation (with minor alterations) of the original MATLAB software created by Alessandro Foi and Markku Mäkitalo (Tampere University of Technology - 2011-2012). Please cite the references below if using this software. http://www.cs.tut.fi/~foi/
References
[1] J.L. Starck, F. Murtagh, and A. Bijaoui, Image Processing and Data Analysis, Cambridge University Press, Cambridge, 1998)
-
crikit.preprocess.algorithms.anscombe.
gen_anscombe_inverse_closed_form
(fsignal, gauss_std, gauss_mean=0, poisson_multi=1)[source]¶ Applies a closed-form approximation of the exact unbiased inverse of the generalized Anscombe variance-stabilizing transformation assuming a mixed Poisson-Gaussian noise model as:
signal = poisson_multi*Poisson{signal0} + Gauss{gauss_mean, gauss_std},
where Poisson{} and Gauss{} are generalized descriptions of Poisson and Gaussian noise.
- Parameters
fsignal (ndarray) – Forward Anscombe-transformed noisy signal (1-,2-,3D)
gauss_std (float, int) – Standard deviation of Gaussian noise
(poisson_multi) (float, int (default = 1)) – Effectively a multiplier that scales the effect of the Poisson noise
(gauss_mean) (float, int (default = 0)) – Mean Gaussian noise level
- Returns
signal – Inverse Anscombe-transformed signal with mixed Gaussian-Poisson noise
- Return type
ndarray (matched to signal shape)
Notes
This software is a direct translation (with minor alterations) of the original MATLAB software created by Alessandro Foi and Markku Mäkitalo (Tampere University of Technology - 2011-2012). Please cite the references below if using this software. http://www.cs.tut.fi/~foi/
References
- [1] M. Mäkitalo and A. Foi, “Optimal inversion of the generalized Anscombe
transformation for Poisson-Gaussian noise”, IEEE Trans. Image Process., doi:10.1109/TIP.2012.2202675
- [2] J.L. Starck, F. Murtagh, and A. Bijaoui, Image Processing and Data
Analysis, Cambridge University Press, Cambridge, 1998)
-
crikit.preprocess.algorithms.anscombe.
gen_anscombe_inverse_exact_unbiased
(fsignal, gauss_std, gauss_mean=0, poisson_multi=1)[source]¶ Applies an exact, unbiased inverse of the generalized Anscombe variance-stabilizing transformation assuming a mixed Poisson-Gaussian noise model as:
signal = poisson_multi*Poisson{signal0} + Gauss{gauss_mean, gauss_std},
where Poisson{} and Gauss{} are generalized descriptions of Poisson and Gaussian noise.
- Parameters
fsignal (ndarray) – Forward Anscombe-transformed noisy signal (1-,2-,3D)
gauss_std (float, int) – Standard deviation of Gaussian noise
(poisson_multi) (float, int (default = 1)) – Effectively a multiplier that scales the effect of the Poisson noise
(gauss_mean) (float, int (default = 0)) – Mean Gaussian noise level
- Returns
signal – Inverse Anscombe-transformed signal with mixed Gaussian-Poisson noise
- Return type
ndarray (matched to signal shape)
Notes
This software is a direct translation (with minor alterations) of the original MATLAB software created by Alessandro Foi and Markku Mäkitalo (Tampere University of Technology - 2011-2012). Please cite the references below if using this software. http://www.cs.tut.fi/~foi/
References
- [1] M. Mäkitalo and A. Foi, “Optimal inversion of the generalized Anscombe
transformation for Poisson-Gaussian noise”, IEEE Trans. Image Process., doi:10.1109/TIP.2012.2202675
- [2] J.L. Starck, F. Murtagh, and A. Bijaoui, Image Processing and Data
Analysis, Cambridge University Press, Cambridge, 1998)
crikit.preprocess.algorithms.arpls module¶
Created on Mon Dec 5 13:53:49 2016
@author: chc
-
class
crikit.preprocess.algorithms.arpls.
ArPlsCvxopt
(smoothness_param=1e-08, redux=1, order=2, fix_end_points=False, max_iter=100, min_diff=1e-05, verbose=False)[source]¶ Bases:
crikit.preprocess.algorithms.abstract_als.AbstractBaseline