aws-package {aws}R Documentation

Adaptive Weights Smoothing

Description

We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing, the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter. The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. <doi:10.18637/jss.v095.i06>, Usage of the package in neuroimaging is illustrated in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.

Details

The DESCRIPTION file:

Package: aws
Version: 2.5-1
Date: 2021-01-11
Title: Adaptive Weights Smoothing
Authors@R: c(person("Joerg","Polzehl",role=c("aut","cre"),email="joerg.polzehl@wias-berlin.de"),person("Felix","Anker",role=c("ctb")))
Author: Joerg Polzehl [aut, cre], Felix Anker [ctb]
Maintainer: Joerg Polzehl <joerg.polzehl@wias-berlin.de>
Depends: R (>= 3.4.0), awsMethods (>= 1.1-1)
Imports: methods, gsl
Description: We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing, the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter. The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. <doi:10.18637/jss.v095.i06>, Usage of the package in neuroimaging is illustrated in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.
License: GPL (>=2)
Copyright: This package is Copyright (C) 2005-2020 Weierstrass Institute for Applied Analysis and Stochastics.
URL: http://www.wias-berlin.de/people/polzehl/
RoxygenNote: 5.0.1

Index of help topics:

ICIcombined             Adaptive smoothing by Intersection of
                        Confidence Intervals (ICI) using multiple
                        windows
ICIsmooth               Adaptive smoothing by Intersection of
                        Confidence Intervals (ICI)
ICIsmooth-class         Class '"ICIsmooth"'
TV_denoising            TV/TGV denoising of image data
aws                     AWS for local constant models on a grid
aws-class               Class '"aws"'
aws-package             Adaptive Weights Smoothing
aws.gaussian            Adaptive weights smoothing for Gaussian data
                        with variance depending on the mean.
aws.irreg               local constant AWS for irregular (1D/2D) design
aws.segment             Segmentation by adaptive weights for Gaussian
                        models.
awsLocalSigma           3D variance estimation
awsdata                 Extract information from an object of class aws
awssegment-class        Class '"awssegment"'
awstestprop             Propagation condition for adaptive weights
                        smoothing
awsweights              Generate weight scheme that would be used in an
                        additional aws step
binning                 Binning in 1D, 2D or 3D
extract-methods         Methods for Function 'extract' in Package 'aws'
gethani                 Auxiliary functions (for internal use)
kernsm                  Kernel smoothing on a 1D, 2D or 3D grid
kernsm-class            Class '"kernsm"'
lpaws                   Local polynomial smoothing by AWS
nlmeans                 NLMeans filter in 1D/2D/3D
paws                    Adaptive weigths smoothing using patches
plot-methods            Methods for Function 'plot' from package
                        'graphics' in Package 'aws'
print-methods           Methods for Function 'print' from package
                        'base' in Package 'aws'
qmeasures               Quality assessment for image reconstructions.
risk-methods            Compute risks characterizing the quality of
                        smoothing results
show-methods            Methods for Function 'show' in Package 'aws'
smooth3D                Auxiliary 3D smoothing routines
smse3ms                 Adaptive smoothing in orientation space SE(3)
summary-methods         Methods for Function 'summary' from package
                        'base' in Package 'aws'
vaws                    vector valued version of function 'aws' The
                        function implements the propagation separation
                        approach to nonparametric smoothing (formerly
                        introduced as Adaptive weights smoothing) for
                        varying coefficient likelihood models with
                        vector valued response on a 1D, 2D or 3D grid.
vpaws                   vector valued version of function 'paws' with
                        homogeneous covariance structure

Author(s)

Joerg Polzehl [aut, cre], Felix Anker [ctb]

Maintainer: Joerg Polzehl <joerg.polzehl@wias-berlin.de>

References

J. Polzehl, K. Tabelow (2019). Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R. Springer, Use R! series. Appendix A. Doi:10.1007/978-3-030-29184-6.

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).

J. Polzehl and V. Spokoiny (2006) Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields 135(3), 335-362. DOI:10.1007/s00440-005-0464-1.

J. Polzehl, V. Spokoiny, Adaptive Weights Smoothing with applications to image restoration, J. R. Stat. Soc. Ser. B Stat. Methodol. 62 , (2000) , pp. 335–354. DOI:10.1111/1467-9868.00235.

V. Katkovnik, K. Egiazarian and J. Astola (2006) Local Approximation Techniques in Signal and Image Processing, SPIE Press Monograph Vol. PM 157

A. Buades, B. Coll and J. M. Morel (2006). A review of image denoising algorithms, with a new one. Simulation, 4, 490-530. DOI:10.1137/040616024.

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]