denoise_tv_bregman
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skimage.restoration.denoise_tv_bregman(image, weight, max_iter=100, eps=0.001, isotropic=True)
[source] -
Perform total-variation denoising using split-Bregman optimization.
Total-variation denoising (also know as total-variation regularization) tries to find an image with less total-variation under the constraint of being similar to the input image, which is controlled by the regularization parameter.
Parameters: image : ndarray
Input data to be denoised (converted using img_as_float`).
weight : float
Denoising weight. The smaller the
weight
, the more denoising (at the expense of less similarity to theinput
). The regularization parameterlambda
is chosen as2 * weight
.eps : float, optional
Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when:
SUM((u(n) - u(n-1))**2) < eps
max_iter : int, optional
Maximal number of iterations used for the optimization.
isotropic : boolean, optional
Switch between isotropic and anisotropic TV denoising.
Returns: u : ndarray
Denoised image.
References
[R328] http://en.wikipedia.org/wiki/Total_variation_denoising [R329] Tom Goldstein and Stanley Osher, “The Split Bregman Method For L1 Regularized Problems”, ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf [R330] Pascal Getreuer, “Rudin–Osher–Fatemi Total Variation Denoising using Split Bregman” in Image Processing On Line on 2012–05–19, http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf [R331] http://www.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf
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