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approximation order p . In addition, we associated with Φ a dual basis, Φ ={ b ,j :
1
b ,j ,b ,j = δ j,j ,1
j,j N . The approximation
j N }
, i.e. one has
Φ is denoted by
order of
p and we assume p p .
Example 12.1.1 (Piecewise linear wavelets) We define the wavelet functions ψ , k as
the following piecewise linear functions. Let h =
a) and c := 3 / 2
2 1 (b
·
( 2 h ) 1 / 2 .For
=
0wehave N 0 =
1 and ψ 0 , 1 is the function with value 2 c 0 at
x
=
a
+
h 0 .For
1thewavelet ψ , 1 has the values ψ , 1 (a
+
h )
=
2 c , ψ , 1 (a
+
2 h )
=−
c and zero at all other nodes. The wavelet ψ , 2 has the values ψ , 2 (b
h )
c and zero at all other nodes. The wavelet ψ ,k with
1 <k< 2 has the values ψ ,k (a
=
2 c , ψ , 2 (b
2 h )
=−
+
( 2 k
2 )h )
=−
c , ψ ,k (a
+
( 2 k
1 )h )
=
2 c ,
ψ ,k (a
0 ,..., 3, these wavelets
are plotted in Fig. 12.1 . The constants c are chosen such that the wavelets ψ ,k are
normalized in L 2 (G) . Note that these biorthogonal wavelets W are not orthogonal
on V 1 . But the inner wavelets ψ ,k with 1 <k< 2 have two vanishing moments,
+
2 kh )
=−
c and zero at all other nodes. For
=
i.e. ψ ,k (x)x n d x =
0for n =
0 , 1. The approximation order of V is p =
2.
Since V L =
span
{
ψ ,k :
0
L, k
∈∇ }
, we have a unique decomposition
L
L
u
=
u =
u ,k ψ ,k ,
=
0
=
0
k ∈∇
H s (G) ,0
for any u
V L with u
W . Furthermore, any u
s
p admits a
representation as an infinite wavelet series,
u
=
u =
u ,k ψ ,k ,
(12.1)
=
0
=
0
k
∈∇
which converges in H s (G) . The coefficients u ,k are the so-called wavelet coeffi-
cients of the function u .
12.1.1 Wavelet Transformation
For u
V L we want to show how to obtain the multi-scale wavelet coefficients
= L = 0 k ∈∇ d ,k ψ ,k , from the single-scale coefficients c ,j in
d ,k :=
u ,k in u
= N L
j =
u
1 c ,j b ,j .
For the wavelets ψ ,k as in Example 12.1.1 and hat functions b ,j ,wehave
1 <k< 2 ,
ψ ,k =−
0 . 5 b , 2 k 2 +
b , 2 k 1
0 . 5 b , 2 k ,
ψ , 1 =
b , 1
0 . 5 b , 2 ,
ψ , 2
=−
0 . 5 b , 2 + 1
+
b , 2 + 1
1 ,
2
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