crikit.preprocess.algorithms package

Submodules

crikit.preprocess.algorithms.abstract_als module

Created on Mon Dec 5 13:44:58 2016

@author: chc

class crikit.preprocess.algorithms.abstract_als.AbstractBaseline[source]

Bases: object

_calc(signal)[source]
calculate(signal)[source]
property fix_rng
setup(redux=1, verbose=False, order=2, rng=None, fix_end_points=False, fix_rng=None, fix_const=1, max_iter=100, min_diff=1e-05, use_prev=True)[source]

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

_calc(signal)[source]

Perform the ALS. Called from self.calculate (defined in AbstractBaseline parent class)

Parameters

signal (ndarray (>= 1D)) – Input signal

Returns

baseline – Baseline of input signal

Return type

ndarray

property asym_param

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

_calc(signal)[source]

Module contents