Wavelets

Report
Wavelets
Applications in Signal and Image Processing
Motivation!
 The Fourier Transform
X( f ) =
¥
ò
-¥
x(t)e-2ip ft dt
Problem
 The FT of stationary and non stationary signals with the same
frequency components are equivalent.
X( f ) » Y (F)
X( f ) =
¥
ò x(t)e
-2ip ft
dt
-¥
Y( f ) =
¥
ò y(t)e
-2ip ft
dt
-¥
 i.e. we are lacking time localization
 Although FT tells us what frequencies appear in the signal it does
not tell us at what time they appear!
What has caused this?
X( f ) =
¥
ò
x(t)e-2ip ft dt
-¥
 e-2iπf is a function of infinite support / infinite window
function
Short Term Fourier Transform: STFT
STFTx(w) (t ', f ) = ò [x(t)w* (t - t ')]e- j 2 p ft dt
t
w(t) = e
(-a(
t2
))
2
 Multiple FT over smaller
windows translated in time
 Compactly supported
 We now have a timefrequency representation
 YOU CAN ALL GO HOME 
NOT!
 Recall:
 In the time domain we know exactly the value of the signal
at any time (time resolution)
 In the frequency domain we know exactly the frequencies in
the signal (frequency resolution)
 In STFT the kernel is compact … thus we can only see a
band of frequencies based on the size of the kernel
Consequence
 Window size is application specific
 Narrow window -> good time resolution, poor frequency resolution
 Wide window -> good frequency resolution, poor time resolution
Increasing window width
Wavelets to the rescue
 We would like to develop a method independent of the
windowing function that gives us
a) Good time resolution and poor frequency resolution at high
frequencies
b) Good frequency resolution but poor time resolution at low
frequencies
 Low frequency => Signal information
 High frequency => Excess detail or noise in the signal
Continuous Wavelet Transform: CWT
yY =
ò
¥
-¥
x(t)Y* (t)dt
 Ψ is the mother wavelet, the shape or choice of this
depend on the properties of the signal we wish to
analyze
Time localization
 Inspect the signal at different time steps
 Introduce a translation parameter, t’, that controls the
translation of the function:
y =
Y
t'
ò
¥
-¥
x(t)Y* (t - t ')dt
Frequency Localization
 Inspect the signal for different frequencies
 Introduce a dilation parameter, s, that controls the scale
of the function:
*æt -t'ö
x(t)
Y
ç
÷ dt
ò -¥
è s ø
1 æt -t'ö
Y s,t ' (t) =
Yç
÷
s è s ø
y
Y
s,t '
1
=
s
y =
Y
s,t '
ò
¥
-¥
¥
x(t)Y *s,t ' (t)dt
Result
Changing
dilation
parameter:
Frequency
Localization
Changing translation parameter: Time Localization
y =
Y
s,t '
ò
¥
-¥
x(t)Y*s,t ' (t)dt
Result
+ve response
Low response
-ve response
0 response
Orthogonality / Orthonormal
 Orthogonal:
b
b
V (x),W (x) = ò V (x).W (x)dx = åV(x)W * (x) = 0
*
a
a
 i.e. 2 functions are, at no place the same or, are symmetric
 Orthonormal:
fi (x), f j (x) = di, j
 So dilations and translations of a wavelet must be
orthonormal to itself so as not to influence the construction
of the coefficients
 These allow for perfect reconstructions of the form
yk = x(t), Y (x) =
k
ò x(t)Y (t)dt
x(t) = å x(t), Y k (x) Y(t)
k
*
k
Inverse Wavelet Transform: ICWT
x(t) =
1
Cg
ò -¥ ò 0 ys,t 'Y s,t ' (t)
¥
¥
dsdt '
s2
Denoise by zeroing out
coefficients
Frequency to time resolution
Low scales / high
frequencies have
good time resolution
but poor frequency
resolution.
High scales / low
frequencies have
good frequency
resolution but poor
time resolution.
 STFT has constant time to frequency resolution as window
size is fixed
Discrete Wavelet Transform: DWT
 The Discrete Wavelet Transform is a sampled version of
the Continuous case with discrete dilation and translation
parameters
 Filters or different cut of frequencies are used to analyze
the signal at different scales or resolutions
 We will be requiring a scaling filter/function and a
wavelet filter/function in this case
 Scaling function – low pass filter - approximation
 Wavelet – high pass filter - details
Discrete wavelet Ψ
 Recall that the CW is defined as:
Y s,t ' (t) =
1 æt -t'ö
Yç
÷
s è s ø
 In a continuous transform we find the inner product over
all scales S and translates t’. However now we must
sample s and t’.
 Logarithmic sampling of s means we need to move in
discrete steps on t’ proportional to the scale s.
æ t - nt0' s0m ö -m/2
-m
'
Y m,n (t) =
Yç
=
s
Y(s
t
nt
÷
0
0
0)
m
m
ø
s0 è s0
1
Dyadic scaling
 Dyadic scaling, choose s0=2 and t0’=1
Y m,n (t) = 2-m/2 Y(2-m t - n)
 Later this will lead to a nice down sampling routine
 DWT to obtain detail coefficients becomes:
Y m,n (t) = 2-m/2 Y(2-m t - n)
ym,n =
ò
¥
-¥
x(t)Y m,n (t)dt
Dyadic scaling
Discrete scaling function Φ
 In the CWT we calculated the set of coefficients ψ over all scales s
and translations t’ on the continuous signal x(t)
 As we are sampling x(t) we cannot have these infinite coefficients.
We need some way of keeping track of what the wavelet
coefficients don’t express.
 Therefore we must define how we sample the signal based on the
current dilation, m, of the wavelet. This is done via a Scaling
function
 We can convolve the signal with the scaling function to get
approximation coefficients
fm,n (t) = 2 -m/2 f (2-m t - n)
j m,n =
ò
¥
-¥
x(t)fm,n (t)dt
Discrete scaling function Φ
Approximation and detail
 Approximation coefficients, ϕ, are produced by applying
the scaling function to the sampled signal. They express
the signal at a lower resolution as if the high frequencies
had been removed
 Detail coefficients, ψ, are produced by applying the
wavelet to the sampled signal. They express the higher
frequency components in the signal.
 Thus a signal is represented as the sum of approximation
and detail coefficients:
x(t) =
¥
åj
n=-¥
f
m0 ,n m0 ,n
(t) +
m0
¥
å åy
m=-¥ n=-¥
m,n
Y m,n (t) = xm0 (t) +
m0
åd
m=-¥
m
(t)
Multi-Resolution Analysis, MRA
Haar example
f (t) = f (2t) + f (2t -1)
ì 1 0 < t <1/ 2
ï
Y(t) = í -1 1 / 2 < t < 1
ï 0
else
î
DWT via Filtering

Filter convolution :
x[n]* h[n] =
¥
å x[k]h[n - k]
k=-¥

H (equivalent to wavelet) is high pass, stripping the signal of its lower band
frequencies thus its coefficients represent high frequency components

G (equivalent to scaling function) is a low pass, stripping the signal of its higher
frequencies thus is passed on to the next scale to remove the next band of high
frequency
DWT via Lifting
 Filters can be transformed in the time or frequency
domain into distinct in-place processing steps on the
signal rather than costly convolutions
 Expressing a wavelet in terms of lifting steps is know as a
Second Generation Wavelet
 Here rather than low and high pass filters we perform a
Prediction step and an Update step
 Prediction – high pass filter – we predict what the signal is
 Update – low pass filter – based on the prediction we
update the signal
Lifting
Haar Lifting example
 Take signal x(t) and split it into odd and even pairs
 As a prediction step take the odd away from the even:
 dj-1= oddj-P(evenj)
 As an update step take the mean value of the odd and
even parts
 Sj-1=evenj+U(dj-1)
d j-1[n] = s j [2n +1]- s j [2n]
d j-1[n] = 2d j-1[n]
1
s j-1[n] = s j [2n]+ d j-1[n]
2
s j-1[n] = 2s j-1[n]
s j-1[n] =
d j-1[n] =
1
d j-1[n]
2
1
s j-1[n]
2
1
s j [2n] = s j-1[n]- d j-1[n]
2
s j [2n +1] = s j [2n]+ d j-1[n]
2D DWT
 Wavelets and scaling functions are orthogonal … hence
separable.
 We can apply the transform in one direction then the
other
Z-transform
 Fourier Series:
X(e jw ) = å x[n]e- jnw
nÎZ
 Z-Transform:
X(z) = å x[n]z-n
nÎZ
 Convolution
W(z) = X(z)Y (z) = å x[n]y[n]z-2n
 Shift Left
Xleft (z) = zX(z)
 Shift Right
Xright (z) = z-1 X(z)
 Down sample
Xdown2 (z) =
 Up sample
Xup2 (z) = X(z 2 )
nÎZ
1
X(z1/2 ) + X(z -1/2 ))
(
2
Lifting to Polyphase
 Split:
X(z) = X0 (z 2 )+ z-1 X1 (z 2 )
 Prediction:
 Update:
P : X1 (z) -T(z)X0 (z)
U : X0 (z)+ S(z)X1 (z)
ùé 1 S (z)
N
úê
úûêë 0
1
é H (z) H (z) ù
00
01
ú
H (z) = ê
êë H10 (z) H11 (z) úû
é Y (z) ù
é X (z) ù
ê 0
ú = H (z)ê 0
ú
êë Y1 (z) úû
êë X1 (z) úû
é k 0
H (z) = ê
êë 0 k -1
ùé
1
0 ù é 1 S0 (z)
úê
ú... ê
úûêë -TN (z) 1 úû êë 0
1
ùé
1
0 ù
úê
ú
úûêë -T0 (z) 1 úû
Filters to Z-transform
1
H 0 (z1/2 )X(z1/2 ) + H 0 (-z1/2 )X(-z1/2 ))
(
2
1
Y1 (z) = ( H1 (z1/2 )X(z1/2 ) + H1 (-z1/2 )X(-z1/2 ))
2
Y0 (z) =
X ' (z) = G0 (z)Y0 (z 2 )+G1 (z)Y1 (z 2 )
Lifting to Filters
é
2
Y
(z
)
0
ê
ê Y1 (z 2 )
ë
ù
é
2
X
(z
)
0
2
ú = H (z )ê
ú
ê X1 (z 2 )
û
ë
ù
ú
ú
û
Filter Results = Polyphase Lifting
H 0 (z) = H 00 (z 2 ) + zH 01 (z 2 )
H1 (z) = H10 (z 2 ) + zH11 (z 2 )
G0 (z) = G00 (z 2 ) + z -1G01 (z 2 )
G1 (z) = G10 (z 2 ) + z -1G11 (z 2 )
Further reading
 Boundary problems!
 Vanishing Moments!
 Wavelet packets
 Second generation wavelets
 Multiwavelets
 Curvelets, ridgelets …
Any Questions?

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