denoise-tv-bregman

denoise_tv_bregman

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 the input). The regularization parameter lambda is chosen as 2 * 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