dwpt.2d {waveslim} | R Documentation |
(Inverse) Discrete Wavelet Packet Transforms in Two Dimensions
Description
All possible filtering combinations (low- and high-pass) are performed to decompose a matrix or image. The resulting coefficients are associated with a quad-tree structure corresponding to a partitioning of the two-dimensional frequency plane.
Usage
dwpt.2d(x, wf = "la8", J = 4, boundary = "periodic")
idwpt.2d(y, y.basis)
Arguments
x |
a matrix or image containing the data be to decomposed. This ojbect must be dyadic (power of 2) in length in each dimension. |
wf |
Name of the wavelet filter to use in the decomposition. By
default this is set to |
J |
Specifies the depth of the decomposition. This must be a number
less than or equal to |
boundary |
Character string specifying the boundary condition. If
|
y |
|
y.basis |
Boolean vector, the same length as |
Details
The code implements the two-dimensional DWPT using the pyramid algorithm of Mallat (1989).
Value
Basically, a list with the following components
w?.?-w?.? |
Wavelet coefficient matrices (images). The first index is
associated with the scale of the decomposition while the second is
associated with the frequency partition within that level. The left and
right strings, separated by the dash ‘-’, correspond to the first |
wavelet |
Name of the wavelet filter used. |
boundary |
How the boundaries were handled. |
Author(s)
B. Whitcher
References
Mallat, S. G. (1989) A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, No. 7, 674-693.
Wickerhauser, M. V. (1994) Adapted Wavelet Analysis from Theory to Software, A K Peters.
See Also
dwt.2d
, modwt.2d
,
wave.filter
.