def gen_anscombe_forward(signal, gauss_std, gauss_mean = 0, poisson_multi = 1):
"""
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 : ndarray (matched to signal shape)
"Anscombe-transformed" signal with an approximate unity standard \
deviation/variance (~ 1)
Note
----
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)
"""
SMALL_VAL = 1
fsignal = 2/poisson_multi * _np.sqrt(_np.fmax(SMALL_VAL,poisson_multi*signal +
(3/8)*poisson_multi**2 +
gauss_std**2 -
poisson_multi*gauss_mean))
# fsignal = _ne.evaluate('2/poisson_multi * sqrt(where(poisson_multi*signal + (3/8)*poisson_multi**2 +\
# gauss_std**2 - poisson_multi*gauss_mean > SMALL_VAL,\
# poisson_multi*signal + (3/8)*poisson_multi**2 +\
# gauss_std**2 - poisson_multi*gauss_mean, SMALL_VAL))')
#fsignal = 2/poisson_multi * _np.sqrt(_np.fmax(SMALL_VAL,fsignal))
return fsignal
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