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 TV_denoising_colour and TGV_denoising_colour and array with third dimension refering to RGB channels.

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, http://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 (URL: http://doi.org/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.


[Package aws version 2.5-1 Index]