| 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 MR imaging is illustrated in Polzehl and Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8>.
Details
The DESCRIPTION file:
| Package: | aws |
| Version: | 2.5-5 |
| Date: | 2024-02-07 |
| 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 MR imaging is illustrated in Polzehl and Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8>. |
| License: | GPL (>=2) |
| Copyright: | This package is Copyright (C) 2005-2024 Weierstrass Institute for Applied Analysis and Stochastics. |
| URL: | https://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.
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.