| Dual-tree Filter Banks {waveslim} | R Documentation |
Analysis and synthesis filter banks used in dual-tree wavelet algorithms.
afb(x, af) afb2D(x, af1, af2 = NULL) afb2D.A(x, af, d) sfb(lo, hi, sf) sfb2D(lo, hi, sf1, sf2 = NULL) sfb2D.A(lo, hi, sf, d)
x |
vector or matrix of observations |
af |
analysis filters. First element of the list is the low-pass filter, second element is the high-pass filter. |
af1,af2 |
analysis filters for the first and second dimension of a 2D array. |
sf |
synthesis filters. First element of the list is the low-pass filter, second element is the high-pass filter. |
sf1,sf2 |
synthesis filters for the first and second dimension of a 2D array. |
d |
dimension of filtering (d = 1 or 2) |
lo |
low-frequecy coefficients |
hi |
high-frequency coefficients |
In one dimension the output for the analysis filter bank (afb)
is a list with two elements
lo |
Low frequecy output |
hi |
High frequency output |
lo |
low-pass subband |
hi[[1]] |
'lohi' subband |
hi[[2]] |
'hilo' subband |
hi[[3]] |
'hihi' subband |
lo |
low-pass subband |
hi |
high-pass subband |
where the dimension of analysis will be half its original length. The
output for the synthesis filter bank along one dimension
(sfb2D.A) will be the output array, where the dimension of
synthesis will be twice its original length.
Matlab: S. Cai, K. Li and I. Selesnick; R port: B. Whitcher
WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY\ {tt http://taco.poly.edu/WaveletSoftware/}
## EXAMPLE: afb, sfb af = farras()$af sf = farras()$sf x = rnorm(64) x.afb = afb(x, af) lo = x.afb$lo hi = x.afb$hi y = sfb(lo, hi, sf) err = x - y max(abs(err)) ## EXAMPLE: afb2D, sfb2D x = matrix(rnorm(32*64), 32, 64) af = farras()$af sf = farras()$sf x.afb2D = afb2D(x, af, af) lo = x.afb2D$lo hi = x.afb2D$hi y = sfb2D(lo, hi, sf, sf) err = x - y max(abs(err)) ## Example: afb2D.A, sfb2D.A x = matrix(rnorm(32*64), 32, 64) af = farras()$af sf = farras()$sf x.afb2D.A = afb2D.A(x, af, 1) lo = x.afb2D.A$lo hi = x.afb2D.A$hi y = sfb2D.A(lo, hi, sf, 1) err = x - y max(abs(err))