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Projection Operator
• Def: The approximated function in the subspace is obtained by the “projection operator”
• As j↑, the approximation gets finer, and
•
2Levery for lim
fffPjj
kjk
kjj
jkj
ffP
V
,,
,
,then
for basis lorthonormaan form If
k
kjkjj xfP )( .,,
Projection Operator (cont)
• In general, it is hard to construct orthonormal scaling functions
• In the more general biorthogonal settings,
kjk
kjj
kj
ffP
dual
,,
,
~,then
~ functions scaling ofset second a have we
1)(~
purpose,ion normalizatfor and ~
,
,
',',,
dxxkj
kkkjkj
Ex: Linear Interpolating
functionhat the )(
pulse Dirac the)()(~
,
,,
x
xxx
kj
kjkj
biorthogonal! kjkjkjk
kjj ffP ,,,,
~, , kjkjkj
kkjj ffP ,,,,
~, ,
Ex: Constant Average-Interpolating
support of
width toalproportioninversely
height with functionsbox :~
,kj
kjkjkjk
kjj ffP ,,,,
~, ,
)1dth intervalWi(ht) average(
)]1,[between under area(
)1()(~
,1
,,
kkf
dxxffk
kkjkj
)(, xkj
j,k
Think …
• What does Pj+1 look like in linear interpolating and constant AI?
• What does Pj look like in other lifting schemes? (cubic interpolating, quadratic AI, …)
Polynomial Reproduction
• If the order of MRA is N, then any polynomial of degree less than N can be reproduced by the scaling functions
• That is,
NpxxP ppj 0for NpxxP pp
j 0for
This is true for all j
Ex: MRA of Order 4
• as in the case of cubic predictor in lifting …– Pj can reproduce x0, x1, x2, and x3 (and any linea
r combination of them)
• …
Interchange the roles of primal and dual …
• Define the dual projection operator w.r.t. the dual scaling functions
• Dual order of MRA:– Any polynomial of degree less than is
reproducible by the dual projection operator
kjk
kjjjkj ffPV ,,,
~,
~ then ,
~for basis a form
~ If
N~
N~
jP~
jP~
NpxxP ppj 0for
~ NpxxP ppj 0for
~
fxPxf
xf
fxfPx
Npf
pj
kkjkj
p
kjp
kkj
kkjkj
pj
p
,~~
,,
,~
,
~,,,
~0for and arbitrary For
,,
,,
,,
dxxgxffNote )()( represents g, space,function In :
,~
, fxPfPx pjj
p ,~
, fxPfPx pjj
p
kjk
kjj ffP ,,
~,
~
fxPfxP pj
pj ,
~,
~1
• From property of :jP~
! moments ~
toup preserve
,,~
,~
,
11
NP
fPxfxP
fxPfPx
j
jpp
j
pjj
p
j+1: one level finerin MRA
This means: “The pth moment of finer and coarsened approximations are the same.”
This means: “The pth moment of finer and coarsened approximations are the same.”
Summary•
• If the dual order of MRA is – Any polynomial of degree less than is reproducible by t
he dual projection operator– Pj preserves up to moments
• If the order of MRA is N– Any polynomial of degree less than N is reproducible by t
he projection operator Pj
– preserves up to N moments
N~
fn scaling dual oforder :~
fn scaling primal oforder :
N
N
N~ jP
~
jP~
N~
~
Subdivision
• Assume
• The same function can be written in the finer space:
• The coefficients are related by subdivision:
kjkjk
kjkj ff ,,,,
~, ,
kjkjk
kjkj ff ,1,1,1,1
~, ,
k
kjlkjlj h ,,,,1 k
kjlkjlj h ,,,,1
Recall “lifting-2.ppt”, p.16, 18
Coarsening
• On the other hand, to get the coarsened signal from finer ones: substitute the dual refinement relation
• into
• Recall
ljl
lkjkj h ,1,,,
~~~
kjkj f ,1,1
~,
l
ljlkjl
ljlkjkjkj fhhff ,1,,,1,,,,
~,
~~~,
~,
l
ljlkjkj h ,1,,,
~ l
ljlkjkj h ,1,,,
~
Wavelets …
• form a basis for the difference between two successive approximations
• Wavelet coefficients: encode the difference of DOF between Pj and Pj+1
Pj
PjPj+1
Pj+1- Pj
This implies …
0,
then
any for 0,,or
~0 ,,
:moments ~
toup preserves if that Note
,
,,1
1
,,1
pkj
p
kkjkj
pjj
pj
pj
j
kkjkjjj
x
fxxfPfP
NpxfPxfP
NP
fPfP
(primal) wavelet has vanishing
momentsN~
MRA
1
0
12
0,,0
1
00
Then n
j mmjmjn
j
n
jn
j
fPfP
WVV
ljl
lmjmjjj
jjj
gVW
WVV
,1,,,1
1
, Since
VN
VN-1 WN-1
VN-2 WN-2
VN-3 WN-3
• Wj depends on …
– how Pj is calculated from Pj+1
• Hence, related to the dual scaling function
orthogonal are waveletsand functions scaling
0~
, is,That
}0{ therefore
and Since
,,
11
Dual
WP
WVVVVP
kjmj
jj
jjjjjj
kj ,
~
kjkj
kkjkj
f
f
,,
,,
~,
0~
,0
~,
~
, ~
,
as drepresente becan in function Any
,,
,,,,
,,,,,,
,,
kjmjjj
kjk j
kjmjmj
kjk
kjmjj
mjkjk
kjj
mjl
mj
j
WP
ggP
g
W
Details
PrimalScaling Fns
mj ,
DualScaling Fns
mj ,
~
basis of
coeff. obtained by
Primal Wavelets
mj ,
moments vanishing~N
jP~
DualProjection
N~
Order
jPPrimalProjection
NOrder
DualWavelets
mj ,~moments vanishingN
basis of
complement(refinement relation)
complement(refinement relation)
Lifting (Basic Idea)
• Idea: taken an old wavelet (e.g., lazy wavelet) and build a new, more performant one by adding in scaling functions of the same level
old waveletsscaling fns at level j
scaling fns at level j+1combine old wavelet with 2 scalingfns at level j to form new wavelet
Lifting changes …
• Changes propagate as follows:
Primalwavelet
DualScaling fn
Pj: Computing Coarser rep.
Dualwavelet
Inside Lifting
• From above figure, we know P determines the primal scaling function (by sending in delta sequence)
• Different U determines different primal wavelets (make changes on top of the old wavelet)
Inside Lifting (cont)
• U affects how sj-1 to be computed (has to do with ). Scaling fns are already set by P.
• ? From the same two-scale relations with (same )
• Visualizing the dual scaling functions and wavelets by cascading
kj ,
~
kj ,
~kj ,~
g~