Python skimage.restoration.denoise_tv_bregman() Examples

The following are 4 code examples of skimage.restoration.denoise_tv_bregman(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module skimage.restoration , or try the search function .
Example #1
Source File: operators.py    From proxalgs with MIT License 5 votes vote down vote up 
def tvd(x0, rho, gamma):
    """
    Proximal operator for the total variation denoising penalty

    Requires scikit-image be installed

    Parameters
    ----------
    x0 : array_like
        The starting or initial point used in the proximal update step

    rho : float
        Momentum parameter for the proximal step (larger value -> stays closer to x0)

    gamma : float
        A constant that weights how strongly to enforce the constraint

    Returns
    -------
    theta : array_like
        The parameter vector found after running the proximal update step

    Raises
    ------
    ImportError
        If scikit-image fails to be imported
    """
    try:
        from skimage.restoration import denoise_tv_bregman
    except ImportError:
        print('Error: scikit-image not found. TVD will not work.')
        return x0

    return denoise_tv_bregman(x0, rho / gamma)
Example #2
Source File: defenses.py    From jpeg-defense with MIT License 5 votes vote down vote up 
def denoise_tv_bregman(img_arr, weight=30):
    denoised = _denoise_tv_bregman(img_arr, weight=weight) * 255.
    return np.array(denoised, dtype=img_arr.dtype)
Example #3
Source File: data_augmentation.py    From Automated-Cardiac-Segmentation-and-Disease-Diagnosis with MIT License 5 votes vote down vote up 
def denoise_img_vol(image_vol, weight=.01):
    denoised_img = slicewise_bilateral_filter(image_vol)
    # denoised_img = denoise_tv_chambolle(image_vol, weight=weight, eps=0.0002, n_iter_max=200, multichannel=False)
    # denoised_img = denoise_tv_bregman(image_vol, weight=1./weight, max_iter=100, eps=0.001, isotropic=True)
    # print (np.linalg.norm(denoised_img-image_vol))
    return denoised_img
Example #4
Source File: pre.py    From skan with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
def threshold(image, *, sigma=0., radius=0, offset=0.,
              method='sauvola', smooth_method='Gaussian'):
    """Use scikit-image filters to "intelligently" threshold an image.

    Parameters
    ----------
    image : array, shape (M, N, ...[, 3])
        Input image, conformant with scikit-image data type
        specification [1]_.
    sigma : float, optional
        If positive, use Gaussian filtering to smooth the image before
        thresholding.
    radius : int, optional
        If given, use local median thresholding instead of global.
    offset : float, optional
        If given, reduce the threshold by this amount. Higher values
        result in fewer pixels above the threshold.
    method: {'sauvola', 'niblack', 'median'}
        Which method to use for thresholding. Sauvola is 100x faster, but
        median might be more accurate.
    smooth_method: {'Gaussian', 'TV'}
        Which method to use for smoothing. Choose from Gaussian smoothing
        and total variation denoising.

    Returns
    -------
    thresholded : image of bool, same shape as `image`
        The thresholded image.

    References
    ----------
    .. [1] http://scikit-image.org/docs/dev/user_guide/data_types.html
    """
    if sigma > 0:
        if smooth_method.lower() == 'gaussian':
            image = filters.gaussian(image, sigma=sigma)
        elif smooth_method.lower() == 'tv':
            image = restoration.denoise_tv_bregman(image, weight=sigma)
    if radius == 0:
        t = filters.threshold_otsu(image) + offset
    else:
        if method == 'median':
            footprint = hyperball(image.ndim, radius=radius)
            t = ndi.median_filter(image, footprint=footprint) + offset
        elif method == 'sauvola':
            w = 2 * radius + 1
            t = threshold_sauvola(image, window_size=w, k=offset)
        elif method == 'niblack':
            w = 2 * radius + 1
            t = threshold_niblack(image, window_size=w, k=offset)
        else:
            raise ValueError('Unknown method %s. Valid methods are median,'
                             'niblack, and sauvola.' % method)
    thresholded = image > t
    return thresholded