Coiflets are discrete wavelets designed by Ingrid Daubechies, at the request of Ronald Coifman, to have scaling functions with vanishing moments. The wavelet is near symmetric, their wavelet functions have
N/3
N/3-1
Some theorems about Coiflets:[3]
For a wavelet system
\{\phi,\tilde{\phi},\psi,\tilde{\psi},h,\tilde{h},g,\tilde{g}\}
\begin{array}{lcl} l{M\tilde{\psi}}(0,l]=0&forl=0,1,\ldots,L-1\\ \sumn(-1)nnlh[n]=0&forl=0,1,\ldots,L-1\\ H(l)(\pi)=0&forl=0,1,\ldots,L-1 \end{array}
\psi
\tilde{h}
For a wavelet system
\{\phi,\tilde{\phi},\psi,\tilde{\psi},h,\tilde{h},g,\tilde{g}\}
\begin{array}{lcl} l{M\tilde{\phi}}(t0,l]=\delta[l]&forl=0,1,\ldots,L-1\\ l{M\tilde{\phi}}(0,l]=
l | |
t | |
0 |
&forl=0,1,\ldots,L-1
t& | |
\\ \hat{\phi} | |
0) |
forl=0,1,\ldots,L-1\\ \sumn
l | |
(n-t | |
0) |
h[n]=\delta[l]&forl=0,1,\ldots,L-1\\ \sumnnl
l | |
h[n]=t | |
0 |
&forl=0,1,\ldots,L-1\\ H(l)
t | |
(0)=(-jt | |
0) |
&forl=0,1,\ldots,L-1\\ \end{array}
and similar equivalence holds between
\tilde{\psi}
\tilde{h}
For a biorthogonal wavelet system
\{\phi,\psi,\tilde{\phi},\tilde{\psi}\}
\tilde{\psi}
\psi
\begin{array}{lcl} l{M\tilde{\psi}}(t0,l]=\delta[l]&forl=0,1,\ldots,\bar{L}-1\\ l{M\psi}(t0,l]=\delta[l]&forl=0,1,\ldots,\bar{L}-1\\ \end{array}
for any
\bar{L}
\bar{L}\llL
Both the scaling function (low-pass filter) and the wavelet function (high-pass filter) must be normalised by a factor
1/\sqrt{2}
Mathematically, this looks like
Bk=(-1)kCN
−10 | −0.0002999290456692 | ||||
−9 | 0.0005071055047161 | ||||
−8 | 0.0012619224228619 | 0.0030805734519904 | |||
−7 | −0.0023044502875399 | −0.0058821563280714 | |||
−6 | −0.0053648373418441 | −0.0103890503269406 | −0.0143282246988201 | ||
−5 | 0.0110062534156628 | 0.0227249229665297 | 0.0331043666129858 | ||
−4 | 0.0231751934774337 | 0.0331671209583407 | 0.0377344771391261 | 0.0398380343959686 | |
−3 | −0.0586402759669371 | −0.0930155289574539 | −0.1149284838038540 | −0.1299967565094460 | |
−2 | −0.1028594569415370 | −0.0952791806220162 | −0.0864415271204239 | −0.0793053059248983 | −0.0736051069489375 |
−1 | 0.4778594569415370 | 0.5460420930695330 | 0.5730066705472950 | 0.5873348100322010 | 0.5961918029174380 |
0 | 1.2057189138830700 | 1.1493647877137300 | 1.1225705137406600 | 1.1062529100791000 | 1.0950165427080700 |
1 | 0.5442810861169260 | 0.5897343873912380 | 0.6059671435456480 | 0.6143146193357710 | 0.6194005181568410 |
2 | −0.1028594569415370 | −0.1081712141834230 | −0.1015402815097780 | −0.0942254750477914 | −0.0877346296564723 |
3 | −0.0221405430584631 | −0.0840529609215432 | −0.1163925015231710 | −0.1360762293560410 | −0.1492888402656790 |
4 | 0.0334888203265590 | 0.0488681886423339 | 0.0556272739169390 | 0.0583893855505615 | |
5 | 0.0079357672259240 | 0.0224584819240757 | 0.0354716628454062 | 0.0462091445541337 | |
6 | −0.0025784067122813 | −0.0127392020220977 | −0.0215126323101745 | −0.0279425853727641 | |
7 | −0.0010190107982153 | −0.0036409178311325 | −0.0080020216899011 | −0.0129534995030117 | |
8 | 0.0015804102019152 | 0.0053053298270610 | 0.0095622335982613 | ||
9 | 0.0006593303475864 | 0.0017911878553906 | 0.0034387669687710 | ||
10 | −0.0001003855491065 | −0.0008330003901883 | −0.0023498958688271 | ||
11 | −0.0000489314685106 | −0.0003676592334273 | −0.0009016444801393 | ||
12 | 0.0000881604532320 | 0.0004268915950172 | |||
13 | 0.0000441656938246 | 0.0001984938227975 | |||
14 | −0.0000046098383254 | −0.0000582936877724 | |||
15 | −0.0000025243583600 | −0.0000300806359640 | |||
16 | 0.0000052336193200 | ||||
17 | 0.0000029150058427 | ||||
18 | -0.0000002296399300 | ||||
19 | −0.0000001358212135 | ||||
F = coifwavf(W) returns the scaling filter associated with the Coiflet wavelet specified by the string W where W = "coifN". Possible values for N are 1, 2, 3, 4, or 5.[4]