Blank line remove

skimage/restoration/deconvolution.py

@@ -172,7 +172,6 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
     -------
     x_postmean : (M, N) ndarray
        The deconvolved data (the posterior mean).
-
     chains : dict
        The keys 'noise' and 'prior' contain the chain list of noise and
        prior precision respectively.
@@ -185,18 +184,14 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
        The stopping criterion: the norm of the difference between to
        successive approximated solution (empirical mean of object
        samples). 1e-4 by default.
-
     burnin : int
        The number of sample to ignore to start computation of the
        mean. 100 by default.
-
     min_iter : int
        The minimum number of iterations. 30 by default.
-
     max_iter : int
        The maximum number of iterations if `threshold` is not
        satisfied. 150 by default.
-
     callback : None
        A user provided callable to which is passed, if the function
        exists, the current image sample. This function can be used to
@@ -314,15 +309,12 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
 def richardson_lucy(data, psf, iterations=50):
     """Richardson-Lucy deconvolution.
 
-
     Parameters
     ----------
     data : ndarray
        The data
-
     psf : ndarray
        The point spread function
-
     iterations : int
        Number of iterations. This parameter play to role of regularisation.
 

skimage/restoration/uft.py

@@ -63,7 +63,6 @@ def _circshift(inarray, shifts):
     ----------
     a : ndarray
        The array to shift.
-
     s : tuple of int
        A tuple of integer scalars where the N-th element specifies the
        shift amount for the N-th dimension of array `a`. If an element
@@ -118,7 +117,6 @@ def ufftn(inarray, dim=None):
     ----------
     inarray : ndarray
         The array to transform.
-
     dim : int, optional
         The `dim` last axis along wich to compute the transform. All
         axes by default.
@@ -142,7 +140,6 @@ def uifftn(inarray, dim=None):
     ----------
     inarray : ndarray
         The array to transform.
-
     dim : int, optional
         The `dim` last axis along wich to compute the transform. All
         axes by default.
@@ -169,7 +166,6 @@ def urfftn(inarray, dim=None):
     ----------
     inarray : ndarray
         The array to transform.
-
     dim : int, optional
         The `dim` last axis along wich to compute the transform. All
         axes by default.
@@ -196,7 +192,6 @@ def uirfftn(inarray, dim=None):
     ----------
     inarray : ndarray
         The array to transform.
-
     dim : int, optional
         The `dim` last axis along wich to compute the transform. All
         axes by default.
@@ -337,15 +332,12 @@ def ir2tf(imp_resp, shape, dim=None, real=True):
     ----------
     imp_resp : ndarray
        The impulsionnal responses.
-
     shape : tuple of int
        A tuple of integer corresponding to the target shape of the
        tranfert function.
-
     dim : int, optional
         The `dim` last axis along wich to compute the transform. All
         axes by default.
-
     real : boolean (optionnal, default True)
        If True, imp_resp is supposed real and the hermissian property
        is used with rfftn Fourier transform.
@@ -396,7 +388,6 @@ def laplacian(ndim, shape):
     ----------
     ndim : int
         The dimension of the laplacian
-
     shape : tuple, shape
         The support on which to compute the transfert function