mra (from subdivision viewpoint)
DESCRIPTION
MRA (from subdivision viewpoint). Jyun-Ming Chen Spring 2001. : record the combined effect of splitting and averaging done to the initial control points to achieve the limit f(x) The same limit curve can be defined from each iteration Using matrix notation. - PowerPoint PPT PresentationTRANSCRIPT
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MRA(from subdivision viewpoint)
Jyun-Ming Chen
Spring 2001
![Page 2: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/2.jpg)
Nested Spaces
• : record the combined effect of splitting and averaging done to the initial control points to achieve the limit f(x)
• The same limit curve can be defined from each iteration
• Using matrix notation
We will show that every subdivision scheme gives rise to refinable scaling functions and, hence, to nested spaces
: some yet undetermined functions;(later we’ll show they are the scaling functions)
![Page 3: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/3.jpg)
Nested Spaces (cont)• Subdivision (refinement)
matrix Pj
– Represent the combined effect of splitting and averaging (both are linear operations)
• refinement relation for scaling functions– Observe the similar relation
for Haar and Daub4
![Page 4: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/4.jpg)
Nested Space (cont)
• The refinement relation states that each of the coarser scaling functions can be written as a linear combination of the finer scaling functions.
• This linear combination depends on the subdivision(refinement) scheme used.
![Page 5: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/5.jpg)
Nested Space
• Define space Vj that includes all linear combinations of scaling functions (of j), whose dimension denoted by v(j)
• Then
• From
Pj is a v(j) by v(j-1) matrix
jjv
jjj xxV 1)(10 ,),(),(span
jj VV 1
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Wavelet Space
• Define wavelet space Wj to be the complement of Vj in Vj+1 ; implying– Any function in Vj+1 can be written as the sum of a unique function i
n Vj and a unique function in Wj
– The dimensions of these spaces are related
• The basis for Wj are called wavelets
• The corresponding scaling function space
![Page 7: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/7.jpg)
• Also write wavelet space Wj as linear combination using basis of next space Vj+1
• From before,
Wavelet Space (cont)
• Combining, this is called the two-scale relation.
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Splitting of MRA Subspaces
VN
VN-1 WN-1
VN-2 WN-2
VN-3 WN-3
![Page 9: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/9.jpg)
Example: Haar
2
1
2
12
1
2
1
210
210
021
021
2121
2121
2121
2121
210210
210210
021021
021021
1000
0100
0010
0001
210210
210210
021021
021021
1)dim(,1)dim(
2)dim(,2)dim(
4)dim(
00
11
2
WV
WV
V
224125379
5
3
7
9
2c
2
2
24
28
1
1
d
c
4
12
0
0
d
c
22211 QP
11100 QP
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Two-Scale Relations (graphically)
2
130
2
131
20
2
130
2
131
20
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Analysis Filters
• Consider the approxi-mation of a function in some subspace Vj
• Assume the function is described in some scaling function basis
• Write these coefficients as a column matrix
• Suppose we wish to create a lower resolution version with a smaller number of coefficients v(j-1), this can be done by
Aj is a constant matrix of dimension v(j-1) by v(j)Tj
jvjj ccc ][ 1)(0
jjcxf )(
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Analysis/Decomposition
• To capture the lost details as another column matrix dj-1
Bj is a constant matrix of dimension w(j-1) by v(j), relating to Aj
• This process is called analysis or decomposition
• The pair of matrices Aj and Bj are called analysis filters
)1()()1( jvjvjw
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Synthesis/Reconstruction
• If analysis matrices are chosen appropriately, original signal can be recovered using subdivision matrices
• This process is called synthesis or reconstruction
• The pair of matrices Pj and Qj are called synthesis filters
![Page 14: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/14.jpg)
Closer Look at Synthesis
• Performing the splitting and averaging to bring cj-1 to a finer scale
• A perturbation by interpolating the wavelets
![Page 15: MRA (from subdivision viewpoint)](https://reader036.vdocuments.site/reader036/viewer/2022062305/56815a73550346895dc7d820/html5/thumbnails/15.jpg)
Filter Bank
• Doing the aforementioned task repeatedly
• Recall Haar (next page)
1100
0011
2
12A
1100
0011
2
12B
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Haar (Analysis)
21
21
2
212100
002121
2
2
212100
002121
24
28
]5379[
cd
cc
c T
A2
B2
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Haar (Synthesis)
2
2
210
210
021
021
24
28
210
210
021
021
5
3
7
9
P2 Q2
2222 ,
:Note
QBPATT
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Relation Between Analysis and Synthesis Filters
• In general, analysis filters are not necessarily transposed multiples of the synthesis filters (as in the Haar case)
1111)( jjjjjj dccxfjjjjjj cBcA 11
jjjjj BA 11
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Analysis & Synthesis Filters
• Dimension of filter matrices– Aj: v(j-1) by v(j);– Bj: w(j-1) by v(j)– Pj: v(j) by v(j-1)– Qj: v(j) by w(j-1)
• Hence are both square
• … and should be invertible
• Combining:
• We get:
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Orthogonal Wavelets
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Orthogonal Wavelets
• Scaling function orthogonal to one another in the same level• Wavelets orthogonal to one another in the same level and in
all scales• Each wavelet orthogonal to every coarser scaling function • Haar and Daubechies are both orthogonal wavelets
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Implication of Orthogonality
• Two row matrices of functions
• Define matrix
• Has these properties:
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Implication of Orthogonality
Changing subscript to j-1
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Orthonormal …
• Scaling functions– Scaling functions are o
rthonormal only w.r.t. translations in a given scale
– Not w.r.t. the scale (because of the nested nature of MRA)
• Wavelets– The wavelets are ortho
normal w.r.t. scale as well as w.r.t. translation in a given scale