Adaptive Weights Smoothing

Description

The package contains R-functions implementing the Propagation-Separation Approach to adaptive smoothing as described in J. Polzehl and V. Spokoiny (2006) Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields 135(3):335-362. and J. Polzehl and V. Spokoiny (2004) Spatially adaptive regression estimation: Propagation-separation approach, WIAS-Preprint 998. Additionally it contains an implementation of selected LPA-ICI pointwise adaptive smoothing algorithms from the book V. Katkovnik, K. Egiazarian and J. Astola (2006). Local Approximation Techniques in Signal and Image Processing, SPIE Press Monograph Vol. PM 157.

Details

Index:

aws                     AWS for local constant models on a grid
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
binning                 Binning in 1D, 2D or 3D
lpaws                   Local polynomial smoothing by AWS
kernsm                  1D, 2D, 3D nonparametric kernel smoothing via fft
ICIsmooth               pointwise adaptive kernel smoothing  
ICIcombined             pointwise adaptive kernel smoothing with fusing

Author(s)

Joerg Polzehl <polzehl@wias-berlin.de>

Maintainer: Joerg Polzehl <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

Results