# encoding: utf-8
# module dipy.denoise.nlmeans_block
# from C:\Users\xukai\Downloads\发票2\venv\Lib\site-packages\dipy\denoise\nlmeans_block.cp311-win_amd64.pyd
# by generator 1.147
# no doc

# imports
import builtins as __builtins__ # <module 'builtins' (built-in)>
import numpy as np # C:\Users\xukai\Downloads\发票2\venv\Lib\site-packages\numpy\__init__.py

# functions

def firdn(*args, **kwargs): # real signature unknown
    """
    Applies the filter given by the convolution kernel 'h' columnwise to
        'image', then subsamples by 2. This is a special case of the matlab's
        'upfirdn' function, ported to python. Returns the filtered image.
    
        Parameters
        ----------
        image: 2D array of doubles
            the input image to be filtered
        h: double array
            the convolution kernel
    """
    pass

def nlmeans_block(*args, **kwargs): # real signature unknown
    """
    Non-Local Means Denoising Using Blockwise Averaging
    
        Parameters
        ----------
        image : 3D array of doubles
            the input image, corrupted with rician noise
        mask : 3D array of doubles
            the input mask
        patch_radius :  int
            similar patches in the non-local means are searched for locally,
            inside a cube of side 2*v+1 centered at each voxel of interest.
        block_radius :  int
            the size of the block to be used (2*f+1)x(2*f+1)x(2*f+1) in the
            blockwise non-local means implementation (the Coupe's proposal).
        h :  double
            the estimated amount of rician noise in the input image: in P.
            Coupe et al. the rician noise was simulated as
            sqrt((f+x)^2 + (y)^2) where f is the pixel value and x and y are
            independent realizations of a random variable with Normal
            distribution, with mean=0 and standard deviation=h
        rician : boolean
            If True the noise is estimated as Rician, otherwise Gaussian noise
            is assumed.
    
        Returns
        -------
        fima: 3D double array
            the denoised output which has the same shape as input image.
    
        References
        ----------
        [1] P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann, C. Barillot,
            "An Optimized Blockwise Non Local Means Denoising Filter for 3D Magnetic
            Resonance Images"
            IEEE Transactions on Medical Imaging, 27(4):425-441, 2008
    
        [2] Pierrick Coupe, Jose Manjon, Montserrat Robles, Louis Collins.
            "Multiresolution Non-Local Means Filter for 3D MR Image Denoising"
            IET Image Processing, Institution of Engineering and Technology, 2011
    """
    pass

def upfir(*args, **kwargs): # real signature unknown
    """
    Upsamples the columns of the input image by 2, then applies the
        convolution kernel 'h' (again, columnwise). This is a special case of the
        matlab's 'upfirdn' function, ported to python. Returns the filtered image.
    
        Parameters
        ----------
        image: 2D array of doubles
            the input image to be filtered
        h: double array
            the convolution kernel
    """
    pass

def _firdn_matrix(*args, **kwargs): # real signature unknown
    pass

def _firdn_vector(*args, **kwargs): # real signature unknown
    pass

def _upfir_matrix(*args, **kwargs): # real signature unknown
    pass

def _upfir_vector(*args, **kwargs): # real signature unknown
    pass

def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown
    pass

# no classes
# variables with complex values

__all__ = [
    'firdn',
    'upfir',
    'nlmeans_block',
]

__loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x0000022DE495EAD0>'

__spec__ = None # (!) real value is "ModuleSpec(name='dipy.denoise.nlmeans_block', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x0000022DE495EAD0>, origin='C:\\\\Users\\\\xukai\\\\Downloads\\\\??2\\\\venv\\\\Lib\\\\site-packages\\\\dipy\\\\denoise\\\\nlmeans_block.cp311-win_amd64.pyd')"

__test__ = {}