aws-package {aws} | R Documentation |
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.
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
Joerg Polzehl [aut, cre], Felix Anker [ctb]
Maintainer: Joerg Polzehl <joerg.polzehl@wias-berlin.de>
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