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"]]