R: TV/TGV denoising of image data

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

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 2.4-0 Index]