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    This method is based on the publication :cite:`levin2007closed`.

    Parameters
    ----------
    image: numpy.ndarray
        Input image with shape :math:`h \times  w \times d`
    alpha: numpy.ndarray
        Input alpha matte with shape :math:`h \times  w`
    regularization: float
        Regularization strength :math:`\epsilon`, defaults to :math:`10^{-5}`
    neighbors: list of tuples of ints
        List of relative positions that define the neighborhood of a pixel
    return_background: bool
        Whether to return the estimated background in addition to the foreground
    foreground_guess: numpy.ndarray
        An initial guess for the foreground image in order to accelerate convergence.
        Using input image by default.
    background_guess: numpy.ndarray
        An initial guess for the background image.
        Using input image by default.
    ichol_kwargs: dictionary
        Keyword arguments for the incomplete Cholesky preconditioner
    cg_kwargs: dictionary
        Keyword arguments for the conjugate gradient descent solver

    Returns
    -------
    F: numpy.ndarray
        Extracted foreground
    B: numpy.ndarray
        Extracted background (not returned by default)

    Example
    -------
    >>> from pymatting import *
    >>> image = load_image("data/lemur/lemur.png", "RGB")
    >>> alpha = load_image("data/lemur/lemur_alpha.png", "GRAY")
    >>> F = estimate_foreground_cf(image, alpha, return_background=False)
    >>> F, B = estimate_foreground_cf(image, alpha, return_background=True)


    See Also
    --------
    stack_images: This function can be used to place the foreground on a new background.
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