Filter implementation of the Haar wavelet Multiresolution approximation in general
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Transcript of Filter implementation of the Haar wavelet Multiresolution approximation in general
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• Filter implementation of the Haar wavelet
• Multiresolution approximation in general
• Filter implementation of DWT
• Applications - Compression
The Story of WaveletsTheory and Engineering Applications
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DWT Using Filtering
a(j-1,2k)
a(j-1, 2k+1)
f(t)
fj-1(t)
fj(t)
a(j,k)
][2
1
12,12,12
1
12,12,1
,
kjkj
kj
aa
kjakjaa
Note that at the next finer level, intervals are half as long, so you need 2k to get the same interval.
h’[n]aj-1,k yj-1,k
]1[][2
1][' nnnh
1,1,1,1 2
1 njnjkj aay kjkj ya 2,1,
h’[n] 2aj-1,k
aj,kaj,2k
Approx. coefficients at any level j can be obtained by filtering coef. at level j-1 (next finer level) by h’[n] and downsampling by 2
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Filter Implementation of Haar Wavelet
We showed that aj,k can be obtained from aj-1,k through filtering by using a filter
]1[][2
1][' nnnh
followed by a downsampling operation (drop every other sample). Similarly, d j,k can also be obtained from aj-1,k using the filter g’[n] followed by down sampling by 2…
]1[][2
1][' nnng
This is called decomposition in the wavelet jargon.
g’[n] 2aj-1,k
dj,kdj,2k
Detail coefficients at any level j can be obtained by filtering approximation coefficients at level j-1 (next finer level) by g’[n] and downsampling by 2
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Decomposition Filters
If we take the FT of h’[n] and g’[n]…
)2/cos()('
)2/cos()('2/
2/
j
j
jejG
ejH
LPF HPF
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Decomposition / Reconstruction Filters
We can obtain the coarser level coefficients aj,k or dj,k by filtering aj-1,k with h’[n] or g’[n], respectively, followed by downsampling by 2.
Would any LPF and HPF work? No! There are certain requirements that the filters need to satisfy. In fact, the filters are obtained from scaling and wavelet functions using dilation (two-scale) equations (coming soon…)
Can we go the other way? Can we obtain aj-1,k from aj,k and dj,k from a set of filters. YES!. This process is called reconstruction.Upsample a(k,n) and d(k,n) by 2 (insert zeros
between every sample) and use filters h[n]=[n]+ [n-1] and g[n]= [n]- [n-1]. Add the filter outputs !
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The Discrete Wavelet Transform
g`[n]
h`[n]
2
2 g`[n]
h`[n]
2
2
2
2
g[n]
h[n]
+
2
2
g[n]
h[n]
+
aj,k
dj+1,k
Decomposition Reconstruction
We have only shown the above implementation for the Haar Wavelet, however, as we willsee later, this implementation – subband coding – is applicable in general.
dj+2,k
aj+2,k
aj+1,kaj+1,k
aj,k
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We see that app. and detail coefficients can be obtained through filtering operations, but where do scaling and wavelet functions appear in the subband coding DWT implementation?
Clearly, these functions are somehow hidden in the filter coefficients, but how?
To find out, we need to know little bit more about these scaling and wavelet functions
j
j
j kkjkjkj tkjdatxtf
0
00)(),()()( ,,,
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Let’s suppose that the function f(t) is sampled at N points to give the sequence f[n], and further suppose that kth resolution is the highest resolution (we will compute approximations at k+1, k+2, …etc. Then:
Multiplying and integrating
MRA on Discrete Functions
1
0,1,1
1
0,1,1
1
0,,
)()(
)(][
N
kkjkj
N
kkjkj
N
kkjkj
tdta
tanf
1
2
dttdtt kjkj )( )( ,1,1 1 2
N
kkjkjkjkj
N
kkjkjkjnj
dtttad
dtttaa
0),1(,,,1
0),1(,,1
)()(
)()(
h[k]
g[k]
1)()(
0)()(
11
11
dttt
dttt
kk
kk
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From MRA to Filters
This substitution gives us level j+1 approximation and detail
coefficients in terms of level j coefficients :
we can put the above expressions in convolution (filter) form as
k
kjkjk
kjkj kgadkhaa ][ and ][ ,),1(,),1(
m
mjkjm
mjkj mkgadmkhaa ]2[ and ]2[~
,),1(
~
,),1(
H~
aj,k
G~
2
21-level of DWT decomposition
h[n]=h[-n], and g[n]=g[-n]~ ~ So where do these filters really come from…?
aj+1,k
dj+1,k
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Dilation / Two-scale Equations
Two scale (dilation) equations for the scaling and wavelet functions determine the filters associated with these functions. In particular:
The coefficients c(n) can be obtained as
Recall that
In some books, h[k]= c(k)/√2. Then the two-scale equation becomes
k
ktkct )2()()( or more generally
k
jj ktkct )2()()2( )1(
)2(),(2)( kttkc
)2(][~
2)( ktkhtk
dxxgxfgf )()(,
2/][][~
,][~
)()(][ ),1(,),1(, kckhkhdtttkh kjkjkjkj
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Similarly, the two-scale equation for the wavelet function:
Then:
Dilation / Two-scale Equations
k
ktkbt )2()()(
k
jj ktkbt )2()()2( )1(
)2(][2)(~
ktkgtk
2/][][~,][~)()(][ ),1(,),1(, kbkgkgdtttkg kjkjkjkj
In some books, g[k]= b(k)/√2. Then the two-scale equation becomes~
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Two-Scale Equations
These two equations determine the coefficients of all 4 filters:
h[n]: Reconstruction, lowpass filter
g[n]: Reconstruction, highpass filter
h[n]: Decomposition, lowpass filter
g[n]: Decomposition, highpass filter The following observations can therefore be made
)2(][2)(~
ntnhtn
)2(][2)(~
ntngtn
~
~
highpass isG 1)()( ,1)( 0,G(0)
lowpass is H 1)()( ,0)(,1)0(
][2
1)G(j 0][][
][2
1)H(j 2][][
22
22
~
~
jGjGG
jHjHHH
engngng
enhnhnh
n
nj
n
nj Note : |H(jw)|= |H(jw)|~
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Quadrature Mirror Filters
It can be shown that
that is, h[] and g[] filters are related to each other:
in fact, that is, h[] and g[] are mirrors of each other, with every other coefficient negated. Such filters are called quadrature mirror filters. For example, Daubechies wavelets with 4 vanishing moments…..
1)()(1)()(
2~2~22 jGjHjGjH
][)1(]1[ ngnLh n
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DB-4 Wavelets
h = -0.0106 0.0329 0.0308 -0.1870 -0.0280 0.6309 0.7148 0.2304
g = -0.2304 0.7148 -0.6309 -0.0280 0.1870 0.0308 -0.0329 -0.0106
h = 0.2304 0.7148 0.6309 -0.0280 -0.1870 0.0308 0.0329 -0.0106
g = -0.0106 -0.0329 0.0308 0.1870 -0.0280 -0.6309 0.7148 -0.2304
~
~
][][ ],[][
][)1(]1[~~
ngngnhnh
ngnLh n
L: filter length (8, in this case)
Matlab command “wfilters()” Use freqz() to see its freq. response
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DWT implementation:Subband Coding
G
H
2
2 G
H
2
2
2
2
G
H
+
2
2
G
H
+
x[n]x[n]
Decomposition Reconstruction
~
~ ~
~
n
high kngnxky ]2[][][~
n
low knhnxky ]2[][][~
k
high kngky ]2[][
k
high kngky ]2[][
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DWT Decomposition
x[n] Length: 512B: 0 ~
g[n] h[n]
g[n] h[n]
g[n] h[n]
2
d1: Level 1 DWTCoeff.
Length: 256B: 0 ~ /2 Hz
Length: 256B: /2 ~ Hz
Length: 128B: 0 ~ /4 HzLength: 128
B: /4 ~ /2 Hz
d2: Level 2 DWTCoeff.
d3: Level 3 DWTCoeff.
…….
Length: 64B: 0 ~ /8 HzLength: 64
B: /8 ~ /4 Hz
2
2 2
22
|H(jw)|
w/2-/2
|G(jw)|
w- /2-/2
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Applications
Detect discontinuities
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Applications
Detect hiddendiscontinuities
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Applications
Simpledenoising
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Compression
DWT is commonly used for compression, since most DWT are very small, can be zeroed-out!
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Compression
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Compression
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Compression - ECG
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ECG - Compression
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ECG- Compression