aws-package: Adaptive Weights Smoothing
In aws: Adaptive Weights Smoothing
aws-package 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 (2019),
Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series.
<doi:10.1007/978-3-030-29184-6_6>.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
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.
aws documentation built on July 9, 2023, 6:07 p.m.
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| aws-package | 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 (2019), Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
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.
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