TV_denoising {aws} | R Documentation |
TV/TGV denoising of image data
Description
Total variation and total generalized variation are classical energy minimizing methods for image denoising.
Usage
TV_denoising(datanoisy, alpha, iter = 1000, tolmean = 1e-06,
tolsup = 1e-04, scale = 1, verbose=FALSE)
TGV_denoising(datanoisy, alpha, beta, iter = 1000, tolmean = 1e-06,
tolsup = 1e-04, scale = 1, verbose=FALSE)
TV_denoising_colour(datanoisy, alpha, iter = 1000, tolmean = 1e-06,
tolsup = 1e-04, scale = 1, verbose=FALSE)
TGV_denoising_colour(datanoisy, alpha, beta, iter = 1000, tolmean = 1e-06,
tolsup = 1e-04, scale = 1, verbose=FALSE)
Arguments
datanoisy |
matrix of noisy 2D image data. In case of |
alpha |
TV regularization parameter. |
beta |
additional TGV regularization parameter. |
iter |
max. number of iterations |
tolmean |
requested accuracy for mean image correction |
tolsup |
requested accuracy for max (over pixel) image correction |
scale |
image scale |
verbose |
report convergence diagnostics. |
Details
Reimplementation of original matlab code by Kostas Papafitsoros (WIAS).
Value
TV/TGV reconstructed image data (2D array)
Author(s)
Joerg Polzehl, polzehl@wias-berlin.de, https://www.wias-berlin.de/people/polzehl/
References
J. Polzehl, K. Papafitsoros, K. Tabelow (2020). Patch-Wise Adaptive Weights Smoothing in R, Journal of Statistical Software, 95(6), 1-27. doi:10.18637/jss.v095.i06.
Rudin, L.I., Osher, S. and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Phys. D, 60, 259-268. DOI: 10.1016/0167-2789(92)90242-F.
Bredies, K., Kunisch, K. and Pock, T. (2010). Total Generalized Variation. SIAM J. Imaging Sci., 3, 492-526. DOI:10.1137/090769521.