wpt.2d(x, crystal.names=NULL, basis=NULL, cost.fun="entropy",
wavelet="s8", n.levels=4, boundary="periodic", precondition=F,
dual=F, analysis.filter=NULL, synthesis.filter=NULL,
filter.reverse=F, scale=NULL, thresh=NULL, p=2, prob=.75)
iwpt.2d(x)
iwpt.2d only, an object of
wpt.2d.
crystal.names for details.
If
crystal.names is supplied, then
basis is ignored.
"best.basis" or
"dwt.2d".
See the respective help files for details.
"entropy",
"threshold",
"sure", and "
lp" are available.
Only used for
"best.basis". See
wp.costs.2d for details.
wavelet is
one, the same wavelet is used for both rows and columns.
See
wavelet.packet for a list of all available wavelet names.
x is divided into
2^(2*n.levels)
nrow(x)/2^n.levels by
ncol(x)/2^n.levels
blocks. For
"best.basis",
n.levels gives the blocking factor for the finest level.
When both
crystal.names and
basis are missing,
2D subband basis of
n.levels is computed.
If
n.levels is bigger than
ml, where
ml is the maximum possible level,
computed from the
max.level function, then
n.levels is set to
ml and
a warning message is given.
boundary is one, the same boundary rule is used for both
row and column.
All the boundary rules listed for
dwt are available except for
"infinite" and
"polynomial". See
dwt for the definitions of these rules.
boundary="interval" only.
See
dwt for details.
dwt for details.
cost.fun.
See the function
cp.costs.2d for details.
(0,2] giving the degree of the
lp norm when
cost.fun is
"lp".
See the function
cp.costs.2d for details.
cost.fun is
"threshold" or
"sure".
See the function
cp.costs.2d for details.
(0,1) used to
compute the threshold for when
cost.fun is
"threshold".
See the function
cp.costs.2d for details.
wpt.2d, inheriting from the class
crystal.matrix.
The object is a matrix of the same size as
x with crystal names as an
attribute.
iwpt.2d
returns an image if
x is an object of class
wpt.2d.
The default optional arguments
n.levels, taper, dct.type, boundary
can be reset using function
wavelet.options. See
wavelet.options for details.
Wickerhauser, M. V. (1994). Adapted Wavelet Analysis from Theory to Software. A. K. Peters Ltd, Wellesley, MA.
xx <- phone-mean(phone) par(mfrow=c(1,2)) thresh <- rep(3.7, 16) dd2 <- dwt.2d(xx, wavelet="s8", n.levels=3) ss1 <- shrink(dd2, thresh) zz1 <- reconstruct(ss1) image(zz1) # reconstructed by DWT cc2 <- wpt.2d(xx, basis="best.basis", wavelet="s8", n.levels=3) ss2 <- shrink(cc2, thresh) zz2 <- reconstruct(ss2) image(zz2) # reconstructed by best basis