notes wavelets

7/29/2019 Notes Wavelets

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4.4.4 Wavelets: Concepts and examples

• What is a wavelet?

A wavelet is a function ( )t ψ of finite energy (L2) whose average is zero:

( ) 0t dt ψ

∞

−∞ =∫ (4.47)

Example 4.9 For the reasons that will become apparent a while later, a useful

wavelet should be a small wave, meaning that it has a compact support , or at least

it approaches zero very quickly as t approaches infinity. However, a wavelet can be either smooth or not so smooth, with or without compact support. See, for

example, the wavelets in Figure 4.21.



2

−0.5 0 0.5 1 1.5

−1

0

1

(a) Haar wavelet: non−smooth, compact support

−5 −4 −3 −2 −1 0 1 2 3 4 5−0.5

0

0.5

1(b) Mexican hat: smooth, non−compact support

−1 −0.5 0 0.5 1 1.5 2

−1

−0.5

0

0.5

1

(c) Daubechies wavelet D4: Continuous, compact support

Figure 4.21

=== MATLAB code for Example 4.9 and Fig. 4.21 ===



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% Inputs: sig -- parameter sigma for the mexican hat wavelet.% N -- length of the Daubechies wavelet (must be even).% Writen by W.-S. Lu, University of Victoria.

% Last modified: Dec. 23, 2002.% Example: w = ex4_9(1,4)

function w = ex4_9(sig,N)

figure(1)

subplot(311)t = -0.5:2/1023:1.5;p1 = [zeros(1,256) ones(1,256) -ones(1,256) zeros(1,256)];plot(t,p1,t,zeros(1,1024))axis([-0.5 1.5 -1.5 1.5])gridtitle('(a) Haar wavelet: non-smooth, compact support')subplot(312)t = -5:10/1023:5;c = 2/((pi 0̂.25)*sqrt(3*sig));p2 = c*(1 - t. 2̂/(sig 2̂)).*exp(-t. 2̂/(2*sig 2̂));plot(t,p2,t,zeros(1,1024))gridtitle('(b) Mexican hat: smooth, non-compact support')figure(2)[h,g,rh,rg]= daub(N);[f,w]=wavelet(rh,rg,9,0);

sz = length(w)-1;t = (1-0.5*N):(N-1)/sz:(0.5*N);



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figure(1)subplot(313)plot(t,w)

axis([1-0.5*N 0.5*N 1.2*min(w) 1.2*max(w)])gridtitle('(c) Daubechies wavelet D4: Continuous, compact support')===============

For a wavelet to be useful in DSP and other fields of engineering and appliedscience, additional features such as those listed below turn out to be desirable:

(a) Generate (through dilation and translation) orthogonal or biorthogonal

basis functions in space L2;

(b) Local in both time and frequency domains;(c) Vanishing moments

( ) 0 for 0,1, , 1k t t dt k K ψ

∞

−∞

= = −∫ (4.48)

• Fourier transform (FT) versus wavelet transform (WT)For a signal

1 2( ) f t L L∈ ∩ , its Fourier transform is defined as

ˆ ( ) ( ) j t f f t e dt

ω ω ∞

−

−∞

= ∫ (4.49)



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which measures the strength of signal oscillations at each frequency ω . A weak

point of the FT is that it does not say when these oscillations actually occur in the

time profile of signal f (t ). The reason for this is that the basis function j t

e

ω −

has aninfinite region of support hence the poorest (zero) resolution in the time domain,

although it has the finest resolution in the frequency domain.

Example 4. 10 Consider two continuous-time signals

1

1(sin 2 sin10 sin 20 ) for 0 15

( ) 3

0 elsewhere

t t t t x t

π π π ⎧

+ + ≤ ≤⎪= ⎨

⎪⎩

and

2

sin 2 for 0 5

sin10 for 5 10( )

sin 20 for 10 15

0 elsewhere

t t

t t x t

t t

π

π

π

≤ ≤⎧⎪ < ≤⎪

= ⎨< ≤⎪

⎪⎩

Both signals x1(t ) x2(t ) contain frequencies 1Hz, 5Hz, and 10 Hz. For x1(t ) these

frequencies are everywhere for t ∈ [0, 15], but for x2(t ) at any given time instant

in [0, 15] it contains only a single frequency (1Hz for t ∈ [0, 5], 5 Hz for t ∈ (5,



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10], and 10 Hz for t ∈ (10, 15]).

To get the spectrum of the signals, we sample them for t ∈ [0, 15] with rate f s = 40Hz, yielding 601 samples for each of the signals denoted by x1[n] and x2[n]

respectively, and perform DFT of the discrete-time signals obtained.

The signals x1(t ) and x2(t ) as well as their spectra are shown in Fig. 4.22. It is

observed that although the time-domain profiles of the signals are quite different,their spectra are similar to each other. In other words, the spectrum provides no

immediate and explicit information as which part(s) of the signal’s time-domain

profile have contributed to the signal’s spectrum strength at a specified frequency.



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0 5 10 15−1

0

1(a) Signal x1(t) on time interval [0, 15s]

0 2 4 6 8 10 12 14 16 18 200

50

100

150(b) Spectrum of x1(t) on frequency interval [0, 20Hz]

0 5 10 15−1

0

1(c) Signal x2(t) on time interval [0,15s]

0 2 4 6 8 10 12 14 16 18 20

0

50

100

150(d) Spectrum of x2(t) on frequency interval [0, 20Hz]

Figure 4.22

=== MATLAB code for Example 4. 10 ===

Ts = 1/40;



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t1 = 0:Ts:5;t2 = (5+Ts):Ts:10;t3 = (10+Ts):Ts:15;

t = [t1 t2 t3];x1 = (sin(2*pi*t)+sin(10*pi*t)+sin(20*pi*t))/3;x21 = sin(2*pi*t1);x22 = sin(10*pi*t2);x23 = sin(20*pi*t3);x2 = [x21 x22 x23];

xf1=fft(x1);xf2=fft(x2);fs = 1/Ts;L = (length(x1)-1)/2;f=0:(fs/2)/L:(fs/2);figure(1)

subplot(411)plot(t,x1)gridtitle('(a) Signal x1(t) on time interval [0, 15s]')subplot(412)plot(f,abs(xf1(1:L+1)))gridtitle('(b) Spectrum of x1(t) on frequency interval [0, 20Hz] ')subplot(413)plot(t,x2)grid

title('(c) Signal x2(t) on time interval [0,15s]')subplot(414)



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plot(f,abs(xf2(1:L+1)))gridtitle('(d) Spectrum of x2(t) on frequency interval [0, 20Hz]')

==========

♦ Remark: At another extreme of the spectrum, we note that Dirac’s delta

function, which has also been popular in signal analysis, has the finest resolution

in the time domain but the poorest resolution in the frequency domain.

Now suppose we have a wavelet function 2( )t Lψ ∈ which satisfies (4.47) and

( ) 1t ψ = . The wavelet transform of f (t ) at time u and scale s is defined as

1

( , ) ( ) ( )

t u f u s f t dt

ss ψ

∞

−∞

−=

∫

(4.50)

From (4.50) we see that a WT of signal f (t ) defines a family of functions rather

than a single function:

For each fixed scale (resolution) s, the WT in (4.50) can be viewed as a linear

filtering process with a filter whose impulse response is given by

1

( ) ( )s

t t

ssψ ψ

−=

(4.51)



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resulting in a time function to describe the “details” or “changes” in signal f (t ) at

scale s. From (4.51) we can compute the transfer function of the filter as

ˆ ˆ( ) ( )

ˆwhere ( ) is the FT of the wavelet function ( ).

ss

t

ψ ω ψ ω

ψ ω ψ

=

ˆSince (0) ( ) 0

ˆˆfor each , we have (0) (0) 0s

t dt

s s

ψ ψ

ψ ψ

∞

−∞= =

= =

∫

This implies that the wavelet filter is a bandpass filter.

Example 4.11

1. Mexican-Hat wavelet2

22

221/ 4

2( ) 1

3

t t t e σ ψ

σ π σ

−⎛ ⎞= −⎜ ⎟⎝ ⎠

whose Fourier transform is given by



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2 25/ 2 1/ 4

2 / 28ˆ ( )

3e

σ ω σ π ψ ω ω −=

The plots of the Mexican hat wavelet (with σ = 1) and its FT are shown in Fig.

4.23. We observe that

♦ ( )t ψ is smooth. It has an infinite support but most of its energy is concentrated

in a rather small region in the time domain.

♦ ˆ ( )ψ ω is bandpass with its largest gain at 1.42 rad./s.

♦ ˆ ( )ψ ω has an infinite support as well, but its energy is concentrated in a small

region in the frequency domain.



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−5 −4 −3 −2 −1 0 1 2 3 4 5−0.5

0

0.5

1(a) Mexican−hat wavelet in the time−domain

Time

−5 −4 −3 −2 −1 0 1 2 3 4 50

0.5

1

1.5

2(b) Mexican−hat wavelet in the frequency domain

Frequency in rad./s

Figure 4.23



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2. Haar wavelet

1 for [0, 1/ 2)( ) 1 for [ 1/ 2, 0)

0 elsewhere

t t t ψ

∈⎧⎪= − ∈ −⎨⎪⎩

(4.52)

`Note that

♦ ( )t ψ is a wavelet with a compact support on interval

[0, 1], and it is normalized to satisfy 2 ( ) 1t dt ψ ∞

−∞=∫ .

♦ The FT of the Haar wavelet is given by

/ 2 24 sin ( / 4)ˆ ( )

j je

ω ω

ψ ω

ω

−

=

Fig. 4.24 depicts the amplitude response of the Haar wavelet filter. We see that it

has an infinite support and a rather poor energy concentration in the frequency

domain.



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−1 −0.5 0 0.5 1 1.5 2−1.5

−1

−0.5

0

0.5

1

1.5(a) Haar wavelet in the time domain

Time

−50 −40 −30 −20 −10 0 10 20 30 40 500

0.2

0.4

0.6

0.8

1 (b) Haar wavelet in the frequency domain

Frequency in rad./s

Figure 4.24

3. Daubechies orthogonal wavelet of length 4

Daubechies’ compactly-supported orthogonal wavelets can be easily generated

using the Daubechies orthogonal filter bank discussed earlier through an iterative

algorithm called the cascade algorithm that will be discussed later. The wavelet



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generated by the Daubechies orthogonal filter bank of length 4 is shown in Fig.

4.25a (see also Fig. 4.21c), and its spectrum is depicted in Fig. 4.25b.

−1 −0.5 0 0.5 1 1.5 2−2

−1

0

1

2(a) Daubechies wavelet (D4) in the time−domain

Time

−50 −40 −30 −20 −10 0 10 20 30 40 500

200

400

600

800

1000

(b) Spectrum of Daubechies wavelet

Frequency in Hz

Figure 4.25

We observe that



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♦ Although not very smooth, the Daubechies wavelet is continuous, and has a

finite region of support.♦ ˆ ( )ψ ω is bandpass.

♦ ˆ ( )ψ ω has an infinite support, but its energy is concentrated in a small region

relative to that of the Haar wavelet. ■

• Scaling functions

Wavelet function acts like a bandpass filter that picks the changes (i.e., details) of

the signal. Obviously, one needs another type of functions (filters) to pick the

“main” content of a signal. This is what a scaling function, which is essentially a

lowpass filter, will do.

Formally, the scaling function, often denoted by ( )t ϕ , is defined by the associated

wavelet function as

2

2 2

1

ˆ ( )ˆ( ) ( )

dss d

sω

ψ ξ ϕ ω ψ ω ξ

ξ

∞ ∞

= =∫ ∫

(4.53)



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We see that the scaling function is essentially an aggregation of wavelets at scales

larger than 1. Once2

( )ϕ ω

is computed using (4.53), ( )t ϕ can be found by the

inverse Fourier transform of ( )ϕ ω

.

Example 4.12

1. The scaling function for the Mexican-hat wavelet is defined by

2 23/ 2 1/ 4

2 / 2

2

2 1ˆ( )

3e

σ ω σ π ϕ ω ω

σ

−= +

See Figs. 4.26a and b for ( )t ϕ and ( )ϕ ω , respectively. Note the lowpasscharacteristics of ( )ϕ ω

.

2. The Haar scaling function can be conveniently defined in the time domain as

1 for [0, 1)( )

0 elsewhere

t t ϕ

∈⎧= ⎨

⎩(4.54)

Plots of ( )t ϕ and ( )ϕ ω

are shown in Fig. 4.27.



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−5 −4 −3 −2 −1 0 1 2 3 4 5

0

0.5

1

1.5

2(b) Mexican−hat scaling function in frequency domain

Frequency in rad./s

−5 −4 −3 −2 −1 0 1 2 3 4 5−0.2

0

0.2

0.4

0.6

0.8

(a) Mexican−hat scaling functionin time−domain

Time

Figure 4.26



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−1 −0.5 0 0.5 1 1.5 2−0.5

0

0.5

1

1.5(a) Haar scaling function in time domain

Time

−50 −40 −30 −20 −10 0 10 20 30 40 500

0.2

0.4

0.6

0.8

1

(b) Haar scaling function in frequency domain

Frequency in rad./s

Figure 4.27

We note that for the Haar scaling function and wavelet:

♦ Both ( )t ϕ and ( )t ψ are very simple and have support on interval [0, 1].

♦ Both ( )t ϕ and ( )t ψ have discontinuities.



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♦ Both ( ) and ( )ϕ ω ψ ω

are continuous and somewhat local.

♦ ( )ϕ ω

exhibits a lowpass characteristic.

1. The Daubechies scaling function associated with Daubechies orthogonal filter

bank of length N can also be generated using the cascade algorithm. The

scaling function has a compact (i.e. finite) region of support on the time

interval [0, N – 1]. Figs 4.28a and b depict the scaling function with N = 4 and

its spectrum, respectively.



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0 0.5 1 1.5 2 2.5 3−2

−1

0

1

2(a) Daubechies scaling function (N = 4) in time−domain

Time

−50 −40 −30 −20 −10 0 10 20 30 40 500

200

400

600

800

1000

1200

(b) Spectrum of Daubechies scaling funtion (N = 4)

Frequency in Hz

Figure 4.28



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Compared to the Haar scaling function, it is observed that the Daubechies scaling

function is continuous in the time domain with improved energy concentration in

the frequency domain.

• Dyadic wavelet transform

In the continuous wavelet function

,

1( ) ( )s u

t ut

ssψ ψ

−=

if one chooses 2 and 2 with , integers j js u k j k − −= = ⋅ , then the wavelet

function becomes

/ 2, ( ) 2 (2 ) for , j j

j k t t k j k ψ ψ = − ∈Z (4.55)

Note that here the integer variable j indicates the scale or resolution, with larger j corresponding to higher resolution; and integer variable k represents the

“location” in time axis.



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So if the wavelet is quite local, then we can estimate how signal f (t ) is changing

itself in a neighborhood of time instant t * at a given resolution level j by

projecting the signal onto one of the basis functions in (4.55) with 2

j

k t

∗= ⋅.

Example 4.13 With ( )t ψ being the Haar wavelet, Fig. 4.29 shows nine basis

functions , ( ) j k t ψ for j = – 1, 0, 1 and k = – 1, 0, 1. We see that

♦ For each fixed resolution which is determined by the value of index j, the basisfunctions , ( ) j k t ψ have the same width of support and can “move” along the t -axis

to anywhere by using an appropriate value of index k .

♦ The resolution can be increased by using an increased value of index j or decreased by using a decreased value of j.



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−5 0 5−2

−1

0

1

2(a): j = −1, k = −1

−5 0 5−2

−1

0

1

2(b): j = −1, k = 0

−5 0 5−2

−1

0

1

2(c): j = −1, k = 1

−5 0 5

−2

−1

0

1

2(d): j = 0, k = −1

−5 0 5

−2

−1

0

1

2(e): j = 0, k = 0

−5 0 5

−2

−1

0

1

2(f): j = 0, k = 1

−5 0 5−2

−1

0

1

2(g): j = 1, k = −1

−5 0 5−2

−1

0

1

2(h): j = 1, k = 0

−5 0 5−2

−1

0

1

2(i): j = 1, k = 1

Figure 4.29



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• Dyadic scaling functions

Similarly, the family of the dyadic scaling functions can be generated by a

“mother” scaling function ( )t ϕ as

/ 2, ( ) 2 (2 ) for , j j

j k t t k j k ϕ ϕ = − ∈Z (4.56)

Suppose the mother scaling function (i.e., function ( )t ϕ ) has a small compact

support, then for fixed indices j and k , the projection of signal f (t ) onto , ( ) j k t ϕ

provides information of signal f (t ) at time t near by the time instant 2 jk − , and the

information you get is accurate up to a resolution of 2 j− (often referred to as

“resolution level j ”).

• Signal approximation by wavelets

Given a signal f (t ) in space L2, one can approximate it in many ways. Essentially,

each method of approximation is associated with a system of basis functions in L2,

say { ( )k b t }. For simplicity let us assume the system { ( )k b t } is orthonormal and

complete in L2, then the signal f (t ) can be represented by the system as



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( ) ( ), ( ) ( )k k k

f t f t b t b t ∑= (4.57)

A truncation of the first K terms of (4.57) can be used as an approximation of theoriginal signal f (t ), i.e.,

1( ) ( ) ( ), ( ) ( )

K

K k k k

f t f t f t b t b t =

∑≈ = (4.58)

Now suppose we have another orthonormal and complete system of basis

functions { ( )k c t }, hence the signal can also be approximated by a similar

truncation:

1

ˆ( ) ( ) ( ), ( ) ( )K

K k k k

f t f t f t c t c t =

∑≈ = (4.59)

The question is, which approximation, (4.58) or (4.59), is better?

To address this question, we need a quantitative measure for the performance of

an approximation. A general consensus is that an orthonormal and complete

system is considered efficient if a small (relative to other systems) number of

coefficients in the orthogonal expansion are found large in magnitude.



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The wavelet theory has shown that basis functions generated from good wavelets

often hold the promise of being efficient systems for signal representation

(compression). As explained below, wavelet and scaling functions come naturally

in signal representations.

Example 4.14 Given a signal f (t ) in L2, first we try to represent it by a staircase

approximation:

( ) ( ) ( )k

f t f k t k ϕ ∞

=−∞≈ −∑ (4.60)

where ( )t ϕ is the Haar scaling function, and f (k ) is the sampled value of f (t ) at t =

k . Fig. 4.30 depicts what happens on interval [–1, 6].



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−1 0 1 2 3 4 5 60

0.5

1

1.5

2

2.5

3

3.5

4Haar approximation of signal f(t) at resolution level j = 0

Time

Figure 4.30

If the above representation is found not accurate enough, we double the sampling

rate to obtain a more accurate representation:

( ) ( ) (2 )2k

k f t f t k ϕ

∞

=−∞≈ −∑ (4.61)



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Note that the Haar scaling functions used in (4.61) are at resolution level j = 1,

while the Haar scaling functions in (4.60) are at resolution level j = 0.

Fig. 4.31 shows an improved approximation of f (t ) on the same interval.

−1 0 1 2 3 4 5 60

0.5

1

1.5

2

2.5

3

3.5

4Haar approximation of signal f(t) at resolution level j = 1

Time

Figure 4.31



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By examining the above figure carefully, we see that the improved (i.e., higher

resolution) approximation can be achieved using the preceding (lower resolution)

approximation plus a properly weighted Haar wavelet function of same (i.e.,lower)

resolution on each interval [k , k +1). For example, it can readily verified that on

the interval t [0, 1)∈ , we can write

( ) (0) (2 ) (0.5) (2 1)

1 1( (0) (0.5)) ( ) ( (0) (0.5)) ( )2 2

f t f t f t

f f t f f t

ϕ ϕ

ϕ ψ

≈ + −

= + + −

In general, it can be shown that the improved approximation of f (t ) as described in

(4.61) can be written as

0 0

1 1( ) ( )

2 2( ) ( ) ( )

2 2

( ) ( ) (4.62)

k k

f k f k f k f k

f t t k t k

f t d t

ϕ ψ ∞ ∞

=−∞ =−∞

⎛ ⎞ ⎛ ⎞+ + − +⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠≈ ⋅ − + ⋅ −∑ ∑

+



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where function f 0(t ) denotes the first sum in (4.62) which is an approximation of

f (t ) at level j = 0, and d 0(t ) denotes the second sum in (4.62) which indicates the

changes (details) in signal f (t ) at resolution level j = 0.

The significance of Eq. (4.62):

♦ It says that if we combine an approximation and together with the details,

which was lost in such an approximation, in an appropriate manner, a moreaccurate signal representation can be achieved.

♦ It indicates that the reverse also holds: A high-resolution signal representation

can be split into a lower resolution approximation plus a lower resolution detail

signal.

In the above example, if we denote the improved approximation (at resolution

level j = 1) as f 1(t ), then (4.62) becomes

1 0 0( ) ( ) ( ) f t f t d t = +

As we can see from the second sum in (4.62), the detail signal d 0(t ) contains many

small-magnitude coefficients that are likely negligible.

But what about the term f 0(t ) ?



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We can apply the same idea of “splitting a signal into lower resolution

approximation and detail components” to f 0(t ) to obtain

0 1 1( ) ( ) ( ) f t f t d t − −= +

where d -1(t ) denotes the details at resolution level j = – 1 , which likely contains

many small-coefficient terms, and f -1(t ) denote the signal’s approximation atresolution j = – 1.

It is important to stress that in practice signals in many DSP applications are of

finite length, often representing the sampled values of continuous-time signals in

certain finite time duration. In such cases, as the decomposition proceeds the totalnumber of terms in lower resolution approximation components decrease at rate

12− .

In general, let the signal of interest be given at a certain resolution level, say, j =

j*, as f j*(t ). Then an efficient representation of f (t ) can be obtained as

( ) ( ) ( )f f d



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* * 1 * 1

* 2 * 2 * 1

* * * 2 * 1

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( ) ( ) (4.63)

j j j

j j j

j K j K j j

f t f t d t

f t d t d t

f t d t d t d t

− −

− − −

− − − −

= +

= + +

== + + + +

4.4.5 Connecting Wavelets to Filter Banks: Mallat’s Multiresolution

Analysis (MRA)

In his celebrated work

S. Mallat, “A theory of multiresolution signal decomposition: the wavelet

representation”, IEEE Trans. Pattern Recognition and Machine Intelligence, vol.

11, pp. 674-693, 1989.

Mallat proposed a rigorous mathematical framework for wavelet representation of

signals. In what follows we briefly describe two key components of his theory,namely, the multiresolution analysis (MRA) and fast discrete wavelet

decomposition algorithm.

M ll ’ M l i l i A l i



34

• Mallat’s Multiresolution Analysis

A sequence { , } j

V j ∈Z of closed subspaces of L2 is said to be a multiresolution

approximation if the following properties hold:

(1) For every 2( , ) , ( ) ( 2 ) j

j j j k f t V f t k V

−∈ ∈ ⇔ − ∈Z

(2) 1For every , j j j V V +∈ ⊂Z

(3) 1For every , ( ) (2 ) j j

j f t V f t V +∈ ∈ ⇔ ∈Z

(4) 2Llim j j

V →∞

=

(5) {0}lim j j

V →−∞

=

(6) There exists ( ) such that { ( ), }t t k k ϕ ϕ − ∈Z is a Riesz basis of subspace V 0.

The rationale of the MRA can be easily understood by using the Haar scaling

function as ( )t ϕ in property (6). One can think of subspace V j as a place that

collects all the signals with resolution level j. If we have to deal with a signal withresolution higher than j, then by property (4) we know that it is contained in some

subspace V k with k > j.

Property (2) implies that { , } jV j ∈Z has a nested structure shown in Fig. 4.32.



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V 1

V 0

V -1

Figure 4.32

♦ There are several immediate conclusions that one can draw from the MRA:

(a) Since { ( ), }t k k ϕ − ∈Z is a basis for V 0,/ 2{2 (2 ), } j j

t k k ϕ − ∈Z is a basis

for V j. Moreover, if { ( ), }t k k ϕ − ∈Z is an orthonormal basis, i.e.,

( ), ( ) ( )t i t k i k ϕ ϕ δ − − = − (4.64)

then so is/ 2

{2 (2 ), } j j t k k ϕ − ∈Z .



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(b) Since0 1( )t V V ϕ ∈ ⊂ , ( )t ϕ cab be expressed in V 1 as

( ) 2 (2 )k k

t h t k ϕ ϕ ∞

=−∞= −∑ (4.65)

Eq. (4.65) is called the dilation equation, which is one of the key equations in

wavelet theory.

♦ The relation between two consecutive subspaces, i.e., 1 j jV V +⊂ , makes one

wonder what is in the gap between the two subspaces? Actually, this “gap” is

where the wavelet function ( )t ψ comes to play: space W j is defined as the

“orthogonal gap” between V j and V j+1:

1 j j jV V W + = ⊕ (4.66)

where ⊕ denotes the orthogonal direct sum. As such, W j carries the detailsnecessary to increase the resolution from level j to level j + 1. Fig. 4.33 illustrates

(4.66) as well as the nested relation in { , } jV j ∈Z .



37

V 1

V 0

W 0

Figure 4.33

♦ Based on (4.66) and MRA properties (4) and (5), we can write

1

1 1

2 (4.67)

j j j

j j j

jk k

j j

V V W

V W W

W

L W

+

− −

=−∞

∞=−∞

= ⊕

= ⊕ ⊕

=

= ⊕⇒ = ⊕

Eq. (4.67) gives an orthogonal decomposition of space L2. It implies that for a

finite-energy signal f (t ) in L2, we have



38

( ) ( )W j j

f t P f t ∞

=−∞∑= (4.68)

where Pwj denote the orthogonal projection operator onto space W j. Since spaces

W j collect details of the signal, many terms in (4.68) are likely small and can

therefore be neglected to obtain an accurate yet efficient representation of f (t ).

Now suppose that subspace W 0 admits a basis { ( ),t k k ψ − ∈Z} which is

generated by a wavelet function ( )t ψ , then we (4.66) implies that

0 1( )t W V ψ ∈ ⊂

which means that ( )t ψ can be expressed as an element in V 1 as

( ) (2 )k

k

t g t k ψ ϕ ∞

=−∞

∑= − (4.69)

This important equation is called the wavelet equation. It follows that once thescaling function ( )t ϕ has been determined, (4.69) can be used to compute the

wavelet function ( )t ψ , provided that the coefficients { , }k g k ∈Z are available.

• Orthogonal Wavelets and Orthogonal Filter Banks



39

Orthogonal Wavelets and Orthogonal Filter Banks

Suppose that { ( ), }t k k ϕ − ∈Z is an orthonormal basis for V 0, and { ( ),t k k ψ − ∈Z}

is an orthonormal basis for W 0. Then the dilation equation (DE) and wavelet

equation (WE), i.e.,

( ) 2 (2 )k

k

t h t k ϕ ϕ ∞

=−∞

∑= − (DE)

and

( ) 2 (2 )k k

t g t k ψ ϕ ∞

=−∞∑= − (WE)

can be used to determine their coefficients:

1( ), ( )22

k

t h t k ϕ ϕ = − and

1( ), ( )22

k

t g t k ψ ϕ = −

Conversely, if we know the coefficients in the dilation and wavelet equations,

then the scaling and wavelet functions can be numerically determined using these

equations through the so-called cascade algorithm (to be discussed shortly).

The number of terms in the DE and WE will be reduced to N for compactly



40

The number of terms in the DE and WE will be reduced to N for compactly

supported wavelet functions such as Haar and Daubechies orthogonal wavelets,

where N is the length of the associated filters H 0( z), etc.

For example, it is easy to examine that for the Haar scaling and wavelet functions,

the DE and WE simply become

1 1( ) 2 (2 ) (2 1)2 2

t t t ϕ ϕ ϕ ⎡ ⎤= + −⎢ ⎥⎣ ⎦(4.70)

and1 1

( ) 2 (2 ) (2 1)2 2

t t t ψ ϕ ϕ ⎡ ⎤

= − −⎢ ⎥⎣ ⎦(4.71)

We see that the coefficients in (4.70) and (4.71) are the impulse responses of the

lowpass and highpass synthesis filters in the orthogonal Haar filter bank,

respectively. This connection holds for any compactly supported orthogonal

scaling and wavelet functions.

In particular, the Daubechies scaling function is defined by

1 N −

∑



41

0

( ) 2 (2 )k

k

t h t k ϕ ϕ =

= −∑ (4.72)

where {hk , k = 0, …, N – 1} are the coefficients of the lowpass synthesis filter

F 0( z).

The following iterative algorithm, known as the cascade

algorithm, can be used to determine the numerical values of the scaling function( )t ϕ on its support interval [0, N – 1]:

1( 1) ( )

0( ) 2 (2 )

N i i

k k

t h t k ϕ ϕ −+

=∑= − (4.73)

for i = 0, 1, …, with(0)

( )t ϕ being the Haar scaling function. Convergence of the

algorithm is deemed when

( 1) ( )

2( ) ( )i i

t t ϕ ϕ ε +

− ≤

for a prescribed tolerance ε . Fig. 4.34 shows the 2nd, 4th and 12th iteration results

of the cascade algorithm as applied to the Daubechies filter of length 4 (D4).

2nd iteration



42

0 0.5 1 1.5 2 2.5 3

0

0.5

1

0 0.5 1 1.5 2 2.5 3

0

0.5

1

4th iteration

0 0.5 1 1.5 2 2.5 3

0

0.5

1

1.512th iteration

Figure 4.34

=== MATLAB code of the cascade algorithm ===



43

g

% This function computes the compactly supported

% scaling and wavelet functions based on the% impulse response of a lowpass wavelet fileter h (̂z)% using the cascade algorithm.% Inputs: h -- coefficents of the lowpass wavelet filter% g -- coefficients of the highpass wavelet filter% epsi -- tolerance (for convergence)

% Outputs: phi -- scaling function on [0, N-1]% psi -- wavelet function on [-N/2+1, N/2]% Example: [h,g,rh,rg] = daub(4); [phi,psi] = cascade(rh,rg,1e-3);% Written by W.-S. Lu, University of Victoria% Last modified: Feb. 18, 1999.

function [phi,psi] = cascade(h,g,epsi)

% Initialize iteration using Haar scaling function

N = length(h);phw = [ones(1,1024) zeros(1,(N-2)*1024)];e = 1;

% Compute scaling function using the cascade iteration algorithm

while e > epsi,

phw20 = [phw(1:2:(N-1)*1024-1) zeros(1,(N-1)*512)];

phw2 = h(1)*phw20;f k 1 N 1



44

for k = 1:N-1,w = h(k+1)*[zeros(1,k*512) phw20(1:(N-1)*1024-k*512)];phw2 = phw2 + w;

endphw2 = sqrt(2)*phw2;e = norm(phw2-phw)/N;phw = phw2;

end

phi = phw;

% Compute wavelet function using the wavelet equation

psw20 = [phw(1:2:(N-1)*1024-1) zeros(1,(N-1)*512)];psw2 = g(1)*psw20;

for k = 1:N-1,w = g(k+1)*[zeros(1,k*512) phw20(1:(N-1)*1024-k*512)];psw2 = psw2 + w;

endpsi = sqrt(2)*psw2;

% Plot the scaling and wavelet functions computed

t = 0:(N-1)/((N-1)*1024-1):N-1;t1 = -N/2+1:(N-1)/((N-1)*1024-1):N/2;figure(1)subplot(121)

plot(t,phi)

axis([0 N-1 1.1*min(phi) 1.1*max(phi)])i (' ')



45

axis('square')gridtitle('scaling function')xlabel('time')subplot(122)plot(t1,psi)axis([-N/2+1 N/2 1.1*min(psi) 1.1*max(psi)])axis('square')

gridtitle('wavelet function')xlabel('time')

Once ( )t ϕ is found, the wavelet function can be obtained using the wavelet

equation1

( 2)

( ) 2 (2 )k

k N

t g t k ψ ϕ =− −

= −∑ (4.74)

where {gk , k = - ( N – 2) , …, 0, 1} are the coefficients of the highpass synthesisfilter F 1( z) with their indices shifted backwards by – ( N – 2).

Note that ( )t ψ has a support on [1- N /2, N /2].

The orthogonal Daubechies wavelets of length N has p = N /2 vanishing moments.

Fig. 4.35 shows the Daubechies scaling and wavelet functions with p = 2, 3, and 4

vanishing moments.



46

• Mallat’s Fast Wavelet Transform (FWT)

Let a finite-energy signal f (t ) be in space V j+1 so that it can be expressed as

( 1) / 2 ( 1) 1( ) 2 (2 ) j j j

k

k

f t a t k ϕ ∞

+ + +

=−∞

= −∑ (4.75)

Since 1 j j jV V W + = ⊕ , f (t ) can be decomposed as

/ 2 ( )

/ 2 ( )

( ) ( ) ( )

where

2 (2 )

2 (2 )

j j

j

j

V W

j j j

V k

k

j j j

W k k

f t P f t P f t

P f a t k

P f d t k

ϕ

ψ

= +

= −

= −

∑

∑

with ( ) and ( )t t ϕ ψ being a pair of orthogonal scaling and wavelet functions.

1 5scaling function N = 4, p = 2 wavelet function N = 4, p = 2



47

0 1 2 3

0

0.5

1

1.5

−1 0 1 2

−1

0

1

0 5

0

0.5

1

scaling function N = 6, p = 3

−2 0 2−1

0

1

wavelet function N = 6, p = 3

0 5

0

0.5

1

scaling function N = 8, p = 4

−2 0 2 4−1

−0.5

0

0.5

1

wavelet function N = 8, p = 4

Figure 4.35

The fast orthogonal wavelet transform proposed by Mallat addresses two basic

i



48

questions:

♦ Given ( 1){ , } jk a k + ∈Z , how to compute ( ){ , } j

k a k ∈Z and( ){ , } j

k d k ∈Z ? (Signal Decomposition)

♦ Given( ){ , } j

k a k ∈Z and

( ){ , } j

k d k ∈Z , how to construct

( 1){ , } j

k a k

+ ∈Z ? (Signal Reconstruction)

For the decomposition,

( ) ( 1) ( 1) (0)

2

( ) ( 1) ( 1) (1)

2

(0) (1)

0 1

( * )(2 )

(4.76)

( * )(2 )

where and are the impulse responses of ( ) and ( ).

j j j

k n k n

n

j j j

k n k n

n

a h a k

d g a k

H z H z

∞+ +

−=−∞

∞+ +

−=−∞

= =

= =

∑

∑

a h

a h

h h

Notice that the operation (2k ) in (4.76) corresponds to the downsampling-by-two

operation in a subband system.

For the reconstruction,



49

( 1) ( ) ( )

2 2

( ) ( )

( ) ( )

( ) ( )

( * )( ) ( * )( ) (4.77)

where

if 2 if 2and0 if 2 1 0 if 2 1

j j j

k k n n k n nk k

j j

j j

j j p p

k k

a h a g d

k k

a k p d k pa d k p k p

∞ ∞+

− −=−∞ =−∞= +

= +

⎧ ⎧= == =⎨ ⎨= + = +⎩ ⎩

∑ ∑

a h d g

Notice that the operation required to generate signals ( ) ( )and j j

a d from( ) ( )

and j j

a d in (4.77) corresponds to the upsampling-by-two operation in asubband system. Therefore, together (4.76) and (4.77) admit a 2-channel filter-

bank interpretation with a( j+1) as its input signal.

Based on (4.76) and (4.77), a K -level fast signal decomposition and reconstruction

can be carried out in an MRA framework as follows, where signal f (t ) correspondsto a( J ) :

It is quite obvious that the above decomposition and reconstruction can be

implemented using an orthogonal filter bank with a K -level tree structure, where

the coefficients of the synthesis lowpass and highpass filters are obtained from

DE d WE ti l



50

DE and WE, respectively.

References1. I. Daubechies, “Orthogonal bases of compactly supported wavelets,” Comm. on Pure and Appl.

Math., vol. 41, pp. 909-996, Nov. 1988.

2. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, PA, 1992.

3. S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE

Trans. PRMI , vol. 11, pp. 674-693, July, 1989.

4. S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed., Academic Press, New York, 1999.

7.1 Wavelet De-Noising

7.1.1 The de-noising problem

Suppose we have a noise-corrupted discrete-time signal x[n], which is modeled as

noisy signal noisesignal

[ ] [ ] [ ] 0,1,..., 1 x n s n w n n N = + = − (7.1)

where s[n] is the noise-free signal, and w[n] is noise with n = 0, 1, …, N – 1.

The de-noising problem is to estimate s[n] using the noise-corrupted observations

x[n]. In what follows we present a de-noising method based on discrete wavelet

transform (or equivalently subband decomposition) of the noisy signal x[n] The



51

transform (or equivalently subband decomposition) of the noisy signal x[n]. The

method was proposed by David Donoho and Ian Johnstone in 1993.

The basic assumptions under which the method works are:(1) The number of samples, N , is large.

(2) The noise w[n] has a normal distribution with zero mean and variance2

σ ,

namely,

1 12 2

0 0

1 1[ ] 0 and [ ]

N N

n n

w n w n N N

σ − −

= =

≈ ≈∑ ∑ (7.2)

7.2.2 Methodology and implementation

As illustrated in Fig. 7.1, the de-noising of a discrete-time signal x[n] starts by

applying a discrete wavelet transform (DWT) to it, resulting in a subband signal

y[n].

oisy

signal x[n] y[n] y[n]^

De-noised

signal x[n]^DWT ThresholdingInverse

DWT

Figure 7.1

From Chapter 4 we know that the DWT of x[n] can be performed by repeatedly



52

From Chapter 4, we know that the DWT of x[n] can be performed by repeatedly

using a two-channel analysis filter bank in a treelike structure. See Fig. 7.2 or Fig.

4.11 for a 3-level case, where the subband signal y[n] is formed as

3 3 2 1[ ]= y a d d d

H 0( z)

d 1

d 2

d 3

a3

H 1( z)

2

2

H 0( z)

H 1( z)

2

2

H 0( z)

H 1( z)

2

2

(n)

Figure 7.2

Next, soft thresholding (or an alternative such as hyperbolic thresholding), thatwill be explained shortly, is applied to the components of y[n] (which are

sometimes called wavelet coefficients). This “shrinks” these wavelet coefficients

according to a prescribed threshold that is determined chiefly by the noise

variance. The modified subband signal is denoted by ˆ[ ] y n .

Finally, the inverse DWT is applied the modified subband signal ˆ[ ] y n to construct

a discrete time signal ˆ[ ]x n which hopefully would offer a less noisy profile of the



53

a discrete-time signal [ ] x n which hopefully would offer a less noisy profile of the

original (clear) signal s[n]. It follows from Chapter 4 that the inverse DWT can be

implemented using a tree type filter-bank structure which is mirror-image

symmetrical to that in Fig. 7.2, where the analysis filters H0 and H1 are replaced

with synthesis filters F0 and F1, respectively, and the downsamplers are replaced

by upsamplers. See Fig. 7.3 for a 3-level inverse DWT.

F 0( z)

d 1

d 2

d 3

a3

F 1( z)

2

F 0( z)

F 1( z)

F 0( z)

F 1( z)

2

22

2

2

x (n)^

Figure 7.3

• Thresholding (shrinkage) policies

There are several ways in which thresholding of a given signal may be performed

for a given threshold δ :

(1) Hard thresholding



54

hard

[ ] if [ ]

[ ] 0 if [ ]

y n y n

y n y n

δ

δ

>⎧

= ⎨ ≤⎩ (7.3a)

(2) Soft thresholding

sof

sgn( [ ])( [ ] ) if [ ][ ]

0 if [ ]t

y n y n y n y n

y n

δ δ

δ − >⎧= ⎨

≤⎩(7.3b)

(3) Hyperbolic thresholding (proposed by B. Vidakovic, also known as “almost

hard thresholding”)

2 2

hyper

sgn( [ ]) [ ] if [ ][ ]

0 if [ ]

y n y n y n y n

y n

δ δ

δ

⎧ − >⎪= ⎨

≤⎪⎩(7.3c)

Example 7.1 For y[n] = 0:1:9 and δ = 1, evaluate yhard, ysoft, and yhyper .

The evaluation results are given in Table 7.1.

Table 7.1: y = 0 : 1 : 9, δ = 1



55

y[n] 0 1 2 3 4 5 6 7 8 9

yhard 0 0 2 3 4 5 6 7 8 9

ysoft 0 0 1 2 3 4 5 6 7 8

yhyper 0 0 1.7321 2.8284 3.8730 4.8990 5.9161 6.9282 7.9373 8.9443

From the above table, it is quite clear as why the hyperbolic shrinkage is regardedas “almost hard”.■

• Determination of threshold δ

An important issue in the wavelet de-noising is to choose an appropriate value

for threshold δ . The universal threshold proposed by Donoho and Johnstone in

1993 has been proven effective: it determines δ as

102log ( ) ˆ N δ σ = (7.4)

where N is the length of the signal, and 2σ̂ is an estimate of the noise variance.

A convenient way to estimate noise variance 2σ is to use the detail subsignal at

the highest resolution i e d1 Let d1 = { (1)d i = 1 N/2} then an estimate of



56

the highest resolution, i.e., d 1. Let d 1 {i

d , i 1, …, N /2}, then an estimate of 2σ can be obtained as

/ 2 / 22 (1) 2 (1)

1

1 1

2 2( ) where mean( )ˆ

( 2)

N N

i i

i i

d d d d N N

σ = =

= − = =−

∑ ∑ d (7.5)

Example 7.2 We now apply the above de-noising method with various waveletsand shrinkage policies to several noise-corrupted signals.

1. Piecewise constant signal (1024 samples, see Fig. 7.4).

Noise: 2(0, )σ N that is a random noise sequence of normal distribution with zero

mean and variance

2σ , where

2σ = 0.0107.

Wavelet: Haar

Shrinkage: Hyperbolic

Threshold: Universal

Results: The signal-to-noise ratio (SNR) before and after de-noising are denoted by SNR before and SNR after , respectively, where

2 before 10

[ ]SNR 20log 27.1056dB

[ ] [ ]

s n

x n s n= =



57

2

2after 10

2

[ ] [ ]

[ ]SNR 20log 41.0253dB

[ ] [ ]ˆ

x n s n

s n

x n s n

−

= =−

The clear, noise-corrupted, and de-noised signals are depicted in Fig. 7.4.

For comparison purposes, the soft shrinkage defined by (7.3b) was also applied,

which with the Haar wavelet yields SNR after = 31.4887 dB. Application of

different wavelets (with hyperbolic shrinkage) were applied and found that the

Daubechies filters D4 yields SNR after = 30.2252 dB and the SNR after offered by

higher-order Daubechies filters were even worse.

2. Doppler signal (1024 samples, see Fig. 7.5)

Noise:2

(0, )σ N with2

σ

= 0.0107.Wavelet: Daubechies D8

Shrinkage: Hyperbolic

Threshold: Universal

Results: The SNR before and after de-noising are evaluated as

2[ ]

SNR 20l 14 0750dBs n



58

2 before 10

2

2after 10

2

[ ]SNR 20log 14.0750dB

[ ] [ ]

[ ]SNR 20log 19.6312dB

[ ] [ ]ˆ

x n s n

s n

x n s n

= =−

= =−

The clear, noise-corrupted, and de-noised signals are depicted in Fig. 7.5.

5

original signal0.4

the noise



59

0 0.5 1

−2

−1

0

1

2

3

4

5

0 0.5 1−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0 0.5 1

−2

−1

0

1

2

3

4

5

noise−contaminated signal

0 0.5 1

−2

−1

0

1

2

3

4

5

denoised signal

Figure 7.4

The Daubechies filters D8 employed in above example can be obtained

using the wavelet toolbox UVi_Wave: the coefficients of Daubechies filters H0,



60

H1, F0, and F1, which are denoted by h0, h1, f 0, and f 1 respectively, can be obtained

as[h0,h1,f0,f1] = daub(length);

where “length” is the length of the D-filters, which must be a positive even integer.

Also note that, when length = 2, the above command returns with the coefficients

of the Haar filters.

For comparison purposes, different wavelets (with hyperbolic shrinkage) were

applied and found that the Haar wavelet yielded SNR after = 15.6099 dB and the

Daubechies D4 offered an SNR after

= 18.0301 dB.

original signal

0 3

0.4the noise



61

0 0.5 1−1

−0.5

0

0.5

1

0 0.5 1−0.3

−0.2

−0.1

0

0.10.2

0.3

0 0.5 1

−1

−0.5

0

0.5

1

noise−contaminated signal

0 0.5 1

−1

−0.5

0

0.5

1

denoised signal

Figure 7.5

MATLAB Code for wavelet de-noising of 1-D signals

% Thi f ti d i i t f l th N 2^ i th l t



62

% This function de-noises an input of length N = 2 n̂ using the wavelet

% shrinkage method in which the "universal" threshold is used.% Note: The function requires the use of UVi_Wave wavelet toolbox.% Inputs: x0 -- Original signal.% r -- Noise signal.% h0 -- Impulse response of the lowpass analysis filter.% h1 -- Impulse response of the highpass analysis filter.

% f0 -- Impulse response of the lowpass synthesis filter.% f1 -- Impulse response of the highpass synthesis filter.% choice: 0 -- Hyperbolic (almost hard) shrinkage.% 1 -- Donoho's soft shrinkage.% Output: y -- Denoised signal.% Written by W.-S. Lu, University of Victoria.

% Last modified March 1, 2003.

function y = deno_uv(x0,r,h0,h1,f0,f1,choice)

x0 = x0(:);r = r(:);

x = x0 + r;Nx = length(x);t = 0:1/(Nx-1):1;

% Handle the not-power-of-2 casep = ceil(log10(Nx)/log10(2));

N = 2 p̂;if N > Nx,

xa = [x;zeros(N-Nx,1)];



63

a [ ; e os( , )];else xa = x;

end

% Wavelet decomposition

xd = wt(xa,h0,h1,p);

% Estimate noise variance

d1 = xd(N/2+1:N);db = mean(d1);s2 = sum((d1-db). 2̂)/(N/2-1);

% Compute Donoho's universal threshold

delta = sqrt(2*log10(N)*s2)

% Soft (or hyperbolic) shrinkage of wavelet coefficients xd

if choice == 1,xs = sign(xd).*max(abs(xd)-delta,zeros(N,1));

elsexs = sign(xd).*sqrt(max(xd. 2̂-delta 2̂,zeros(N,1)));

end

% Reconstruct the denoised signal

y1 = iwt(xs,f0,f1,p);



64

y ( , , ,p);y = y1(1:Nx);

y = y(:);

% Performance evaluation

nf = norm(x0);disp('signal-to-noise ratio before denoising:')

SNR_before = 20*log10(nf/norm(r))disp('signal-to-noise ratio after denoising:')SNR_after = 20*log10(nf/norm(y-x0))

% Plot the resultsmi = min([min(x0) min(x) min(y)]);mi = (1 - 0.1*sign(mi))*mi;ma = max([max(x0) max(x) max(y)]);ma = (1 + 0.1*sign(ma))*ma;figure(1)subplot(221)

plot(t,x0)title('original signal')axis([0 1 mi ma])axis('square')gridsubplot(222)

plot(t,r)

title('the noise')axis('square')grid



65

gsubplot(223)

plot(t,x)title('noise-contaminated signal')axis([0 1 mi ma])axis('square')gridsubplot(224)

plot(t,y)title('denoised signal')axis([0 1 mi ma])axis('square')grid

7.1.3 Wavelet de-noising of still images

Extension of the wavelet de-noising method described in Sec. 7.1.2 to still images

is straightforward.

The noisy image is modeled by

noisy image noise

image

[ , ] [ , ] [ , ] , 0,1,..., 1 x i j s i j w i j i j N = + = − (7.6)

As explained in Fig. 7.6, the wavelet de-noising method in this case begins by

applying a two-dimensional (2-D) DWT to the noisy image x[i, j] that yields a 2-

D subband signal y[i j]; it then applies thresholding (shrinkage) to the subband



66

D subband signal y[i, j]; it then applies thresholding (shrinkage) to the subband

signal y[i, j] pointwisely using one of the shrinkage policies described in (7.3a) to(7.3c) to yield ˆ[ , ] y i j ; finally, the 2-D inverse DWT is applied to the modified

subband signal ˆ[ , ] y i j to construct image ˆ[ , ] x i j which would hopefully be a less

noisy profile of the clear digital image s[i, j].

oise-corrupted

image x[i, j]

y[i, j] y[i, j]^

De-noised

image x[i, j]^2-D DWTShrinkage of

Wavelet Coefficients

2-D Inverse

DWT

Figure 7.6

• 2-D discrete wavelet transform (2-D subband decomposition)

The concept of subband decomposition of discrete-time signals can be extended to

two-dimensional signals such as still images in a straightforward manner.

To this end, we need a subband decomposition-reconstruction framework similar

to what has been developed for 1-D signals, with straightforward modifications in

that a 2-D filter-bank building block replaces the one-dimensional filter-bank in

both the analysis filter-bank tree and the synthesis filter-bank tree. Fig. 7.7 depicts

a 2-level 2-D analysis filter bank tree that performs a 2-level subband

decomposition for a still image



67

decomposition for a still image.

H0

H0

H0

H0

H0

H0

H1

H1

H1

H1

H1

H1

2

2

2

2

2

2

2

2

2

2

2

2

Level 1 Level 2

Input Vertical Horizontal Vertical Horizontal

a(2)

a(1)

d 01(2)

d 11(2)

d 10(2)

d 01(1)

d 11(1)

d 10(1)

{ x(i, j)}

N N

N 2

N 2

N 4

N 4

N 4

N 2

N 2

N

Figure 7.7

To illustrate how it works, let input signal { [ , ],0 1,0 1} x i j i N j N ≤ ≤ − ≤ ≤ −

represent an image of size N × N , where the value of x[i, j] is the gray scale of the

image pixel at position [i j] So it is of convenience to treat a digitized image as a



68

image pixel at position [i, j]. So it is of convenience to treat a digitized image as a

matrix, just like a digitized 1-D signal is treated as a vector . At eachdecomposition level, we see a basic 2-D analysis filter bank that consists of 3 two-

channel 1-D filter banks, where the first 2-channel filter-bank filters and

downsamples the input image column by column. As a result, it outputs an

approximate image of size N /2 × N and a detail image of the same size. Then

each of these images is filtered by a 2-channel filter bank row by row, yielding 4

subimages of size N /2 × N /2 that are denoted as (1) (1) (1) (1)

01 10 11, , , and a d d d . This

completes the first-level 2-D subband decomposition. If necessary, the

approximation subimage a(1) can be further decomposed using the same filter

bank module described above to obtain subimages(2) (2) (2) (2)

01 10 11, , , and a d d d of size N /4 × N /4. Thus a two-level 2-D subband decomposition yields a total of 7

subimages. These subimages can be put together as shown in Fig. 7.8 to form a

two-level 2-D subband decomposition of the input image. For an image of size N

× N with N = 2K , a K -level 2-D subband decomposition (2-D DWT) cab be

performed with this tree-structured analysis filter banks.

a(2)d 01

(2)

d(1)



69

d 11(2)d 10(2)

d 01( )

d 11(1)

d 10(1)

Figure 7.8

Also similar to the 1-D case, the 2-D inverse DWT can be performed using a tree-structure 2-D synthesis filter bank that is mirror-image symmetrical to the analysis

filter bank in Fig. 7.7, where the filters H0 and H1 are replaced with F0 and F1,

respectively, and the downsamplers are replaced by upsamplers. See Fig. 7.9 for a

two-level 2-D inverse DWT.

• Determination of threshold δ

Let the input image be of size N × N , Donoho’s universal threshold is then given

by

10ˆ2 log N δ σ = (7.7)

h2ˆ i ti t f th i i th t b bt i d i th



70

where2

σ is an estimate of the noise variance that can be obtained using the

highest resolution detail subimage (1)11 d as

F0

F0

F0

F0

F0

F0

F1

F1

F1

F1

F1

F1

2

2

2

2

2

2

2

2

2

2

2

2

Level 1

Vertical OutputVertical Horizontal

Level 2

Horizontal

N N

N 2

N 2

N 4

N 4

N 4

N 2

N 2

N

a(2)

d 01(2)

d 11(2)

d 10(2)

d 01(1)

d 11

(1)

d 10(1)

{ x(i, j)}^

Figure 7.9

/ 2 / 22 (1) 2

1121 1

4ˆ [ ( , ) ]

4

N N

i j

d i j d N

σ = =

= −−

∑∑



71

/ 2 / 2(1)

1121 1

4( , )

N N

i j

d d i j N = =

= ∑∑

(7.8)

Example 7.3 We now apply the above de-noising method with hyperbolicshrinkage and several wavelets to a 8-bit gray-scale image ‘fruits” of size 256 ×

256.

Noise:2

(0, )σ N with σ = 25.5953.

Wavelet: MIT 9/7

Shrinkage: HyperbolicThreshold: Universal

where the ‘MIT 9/7’ wavelet is characterized by a linear-phase lowpass analysis

filter H0 of length 9 and a linear-phase lowpass synthesis filter F0 of length 7

whose coefficients are given by

0

2[1 0 8 16 46 16 8 0 1]

64= − − h



72

0

2[ 1 0 9 16 9 0 1]

32= − − f

For completeness, the highpass analysis and synthesis filters are characterized by

1

1

2[ 1 0 9 16 9 0 1]

32

2[ 1 0 8 16 46 16 8 0 1]

64

= − − −

= − − −

h

f

Results: The signal-to-noise ratio (SNR) before and after de-noising are given by

before 10

[ , ]SNR 20log 15.2327dB

[ , ] [ , ]F

F

s i j

x i j s i j= =

−



73

after 10

[ , ]SNR 20log 22.2289dB

ˆ[ , ] [ , ]F

F

s i j

x i j s i j= =

−

where [ , ]F

x i j is the Frobenius norm of matrix { x[i, j]} defined by

2

1 1

[ , ] [ , ] N N

F i j

x i j x i j

= =

= ∑∑

The original, noise-corrupted, and de-noised images are depicted in Fig. 7.10.

origimal image noise−contaminated image



74

denoised image

Figure 7.10

References:1. D. Donoho, I. Johnstone, G. Kerkyacharian, and D. Picard, “Wavelet shrinkage:

Asymptopia?” J . Roy. Stat . Soc., vol. 57, pp. 301-369, 1995.

2. D. Donoho, “De-noising by soft thresholding,” IEEE Trans. Information

Theory, vol. 41, pp. 613-627, 1995.

notes wavelets

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