Single-level inverse discrete 1-D wavelet transform
X = idwt(cA,cD,
'wname'
)
X = idwt(cA,cD,Lo_R,Hi_R)
X = idwt(cA,cD,'wname'
,L)
X
= idwt(cA,cD,Lo_R,Hi_R,L)
idwt(cA,cD,'wname'
)
X = idwt(...,'mode'
,MODE)
X = idwt(cA,[],...)
X = idwt([],cD,...)
The idwt
command performs
a single-level one-dimensional wavelet reconstruction with respect
to either a particular wavelet ('wname'
,
see wfilters
for more information)
or particular wavelet reconstruction filters (Lo_R
and Hi_R
)
that you specify.
X = idwt(cA,cD,
returns
the single-level reconstructed approximation coefficients vector 'wname'
)X
based
on approximation and detail coefficients vectors cA
and cD
,
and using the wavelet 'wname'
.
X = idwt(cA,cD,Lo_R,Hi_R)
reconstructs
as above using filters that you specify.
Lo_R
is the reconstruction low-pass
filter.
Hi_R
is the reconstruction high-pass
filter.
Lo_R
and Hi_R
must be
the same length.
Let la
be the length of cA
(which
also equals the length of cD
) and lf
the
length of the filters Lo_R
and Hi_R
;
then length(X) = LX
where LX = 2*la
if
the DWT extension mode is set to periodization. For the other extension
modes LX = 2*la-lf+2
.
For more information about the different Discrete Wavelet Transform
extension modes, see dwtmode
.
X = idwt(cA,cD,
or 'wname'
,L)X
= idwt(cA,cD,Lo_R,Hi_R,L)
returns the length-L
central
portion of the result obtained using idwt(cA,cD,
. 'wname'
)L
must
be less than LX
.
X = idwt(...,
computes
the wavelet reconstruction using the specified extension mode 'mode'
,MODE)MODE
.
X = idwt(cA,[],...)
returns the single-level
reconstructed approximation coefficients vector X
based
on approximation coefficients vector cA
.
X = idwt([],cD,...)
returns the single-level
reconstructed detail coefficients vector X
based
on detail coefficients vector cD
.
Starting from the approximation and detail coefficients at level j, cAj and cDj, the inverse discrete wavelet transform reconstructs cAj−1, inverting the decomposition step by inserting zeros and convolving the results with the reconstruction filters.
Daubechies, I. (1992), Ten lectures on wavelets, CBMS-NSF conference series in applied mathematics. SIAM Ed.
Mallat, S. (1989), “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp. 674–693.
Meyer, Y. (1990), Ondelettes et opérateurs, Tome 1, Hermann Ed. (English translation: Wavelets and operators, Cambridge Univ. Press. 1993.)