Module « scipy.signal »

Fonction wiener - module scipy.signal

Signature de la fonction wiener

def wiener(im, mysize=None, noise=None) 

Description

wiener.__doc__

    Perform a Wiener filter on an N-dimensional array.

    Apply a Wiener filter to the N-dimensional array `im`.

    Parameters
    ----------
    im : ndarray
        An N-dimensional array.
    mysize : int or array_like, optional
        A scalar or an N-length list giving the size of the Wiener filter
        window in each dimension.  Elements of mysize should be odd.
        If mysize is a scalar, then this scalar is used as the size
        in each dimension.
    noise : float, optional
        The noise-power to use. If None, then noise is estimated as the
        average of the local variance of the input.

    Returns
    -------
    out : ndarray
        Wiener filtered result with the same shape as `im`.

    Examples
    --------

    >>> from scipy.misc import face
    >>> from scipy.signal.signaltools import wiener
    >>> import matplotlib.pyplot as plt
    >>> import numpy as np
    >>> rng = np.random.default_rng()
    >>> img = rng.random((40, 40))    #Create a random image
    >>> filtered_img = wiener(img, (5, 5))  #Filter the image
    >>> f, (plot1, plot2) = plt.subplots(1, 2)
    >>> plot1.imshow(img)
    >>> plot2.imshow(filtered_img)
    >>> plt.show()

    Notes
    -----
    This implementation is similar to wiener2 in Matlab/Octave.
    For more details see [1]_

    References
    ----------
    .. [1] Lim, Jae S., Two-Dimensional Signal and Image Processing,
           Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548.