nd.dwt.2d(x, wavelet="s8", n.levels=NULL, boundary="periodic",
dual=F, analysis.filter=NULL, synthesis.filter=NULL)
"d4", "s8",
see
wavelet for all available wavelet names.
If the length of
wavelet is one, the same wavelet is used for both
row and column. See
wavelet for details.
For user-provided filter, input the values in
analysis.filter below.
boundary is one, the same boundary rule is used for both
row and column.
The only available rule currently is:
`"periodic".
wavelet for details.
filter argument in
wave.filter for
details.
filter argument in
wave.filter for
details. When
analysis.filter is provided, then the default
synthesis.filter
is also
analysis.filter.
nd.dwt.2d, inheriting from the classes
dwt.2d
,
wpt.2d, and
crystal.matrix.
See
crystal.matrix.object for details.
The non-decimatedtwo dimensional discrete wavelet transform is non-orthogonal
variant to the classical 2-D DWT.
With the non-decimated DWT, starting with
nr x nc sample values,
you end up with
(3.J+1) nr.nc coefficients.
Unlike the classical 2-D DWT,
which has fewer coefficients at coarse scales,
each scale for the non-decimated DWT has
3.(nr.nc) coefficients.
The non-decimated wavelet
transform can be inverted using the
reconstruct function.
Refer to the section "Non-Decimated Wavelets" in the
S+WAVELETS User's Manual
for more details about the
nd.dwt.2d function.
All the default optional arguments can be reset using function
wavelet.options
. See
wavelet.options for details.
Under
"periodic" boundary rule (the only boundary rule currently supported),
matrix
x is assumed to be periodic.
Mallat, S. and Hwang, W. L. (1992). Singularity Detection and Processing with Wavelets. IEEE Transactions on Information Theory, 38 (2), 617-643. Shensa, M. J. (1992). The Discrete Wavelet Transform: Wedding the A Trous and Mallat Algorithms. IEEE Transactions on Signal Processing, 40 (10), 2464-2482.
nd.brain <- nd.dwt.2d(brain, n.levels=2) image(nd.brain[["s1-s1"]]