R: Adaptive Weights Smoothing

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 as described in "J. Polzehl and V. Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1>", "J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>" and "J. Polzehl, K. Papafitsoros, K. Tabelow (2018) <DOI:10.20347/WIAS.PREPRINT.2520>", the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.

### Details

The DESCRIPTION file:

Package: | aws
Version: | 2.2-1
Date: | 2019-04-23
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), methods, awsMethods (>= 1.0-1), 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 as described in "J. Polzehl and V. Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1>", "J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>" and "J. Polzehl, K. Papafitsoros, K. Tabelow (2018) <DOI:10.20347/WIAS.PREPRINT.2520>", the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
License: | GPL (>=2)
Copyright: | This package is Copyright (C) 2005-2018 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.
    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'
    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'
    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 and V. Spokoiny (2006) Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields 135(3), 335-362.

J. Polzehl and V. Spokoiny (2004) Spatially adaptive regression estimation: Propagation-separation approach, WIAS-Preprint 998.

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

[Package aws version 2.2-1 Index]