Wavelet Toolware - EBM English Books

Wavelet Toolware - EBM English Books
Wavelet Toolware:
Software for Wavelet Training
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Wavelet Toolware:
Software for Wavelet Training
Andrew K. Chan
Department of Electrical Engineering
Texas A&M University
Steve J. Liu
Department of Computer Science
Texas A&M University
San Diego London Boston
New York Sydney Tokyo Toronto
This book is printed on acid free paper.
Copyright © 1998 by Academic Press
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This eBook does not include the ancillary media that was
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Part I
From Fourier Analysis to Wavelet Analysis
Short-Time Fourier Transform
Window Measures
What Is a Wavelet?
1.4.1 Wavelets at different resolutions (scales)
Continuous Wavelet Transform
Multiresolution Analysis
1.6.1 Two-scale relations
1.6.2 Decomposition relation
1.6.3 Development of the decomposition and reconstruction algorithms
1.6.4 Mapping of functions into the approximation space
Types of Wavelets
1.7.1 Orthonormal wavelet bases
1.7.2 Semi-orthogonal wavelet bases
1.7.3 Biorthogonal wavelet bases
Wavelet Packets
1.8.1 Wavelet packet algorithms
Two-Dimensional Wavelets
1.9.1 Two-dimensional wavelet packets
Part II
Wavelet Algorithms Overview
Algorithm for Computing the STFT
Algorithm for Computing the CWT
Computation and Display of Scaling Functions
Computation and Graphical Display of Wavelets
Mapping between B-Splines and Their Duals
Decomposition of a 1-D Signal
Reconstruction of 1-D Signals
2.9.1 Hard thresholding
2.9.2 Soft thresholding
2.9.3 Quantile thresholding
2.9.4 Universal thresholding
2.10 Two-Dimensional Extension of Wavelet Algorithms ...
2.11 Other Algorithms
Part III
3.1 The Wavelet Toolware Overview
3.1.1 Installation of Toolware
3.1.2 Wavelets
3.2 Using Toolware
3.3 2-DDWTTool
3.3.1 Data file selection
3.3.2 Wavelet decomposition
3.3.3 Display thresholding
3.3.4 Wavelet reconstruction
3.4 1-D DWT Tool
3.4.1 1-D DWT decomposition
3.4.2 Viewing and processing the data
3.5 Continuous Wavelet Transform Tool
3.6 Short Time Fourier Transform Tool
3.7 Wavelet Builder Tool
Wavelet Toolware is a companion software package to the book An
Introduction to Wavelets by Charles K. Chui. It is designed for the
reader to gain some hands-on practice in the subject of wavelets. The
objective is to provide basic signal analysis and synthesis tools that are
flexible enough for the reader to easily increase the capability of the
Toolware by adding processing algorithms to tailor to specific areas of
application. Wavelet Toolware contains, among other algorithms and
codes, the one-dimensional (1-D) and two-dimensional (2-D) Discrete
Wavelet Transforms (DWTs) and their inverse transforms, as well as
computations of the Continuous Wavelet Transform (CWT) and the
Short Time Fourier Transform (STFT). Simple signal processing applications are also included for reader to practice with the software.
This manual for Wavelet Toolware leads the reader through the basics of wavelet theory and shows how it can be applied to a number of
engineering problems. It is organized into three major divisions. Part
I concerns the basics of wavelet theory, and readers should get a very
fundamental understanding of the development of the theory and gain
appreciation of the time-frequency (or time-scale) representation of signals. Part II contains several major algorithms that correspond to the
topics discussed in Part I. In most cases, the algorithms are outlined
in procedural steps. Finally, Part III provides hands-on practice with
the Wavelet Toolware. It contains many practice sections ranging from
installation of the software to denoising of images.
This manual is designed to be self-contained for the reader who is
unfamiliar with wavelet theory but wants to gain a basic understanding
and the usage of wavelet theory. After thoroughly studying this manual and practicing with the Toolware, readers should be able to apply
the basic aspects of wavelet analysis to problems of their respective
Andrew K. Chan
Steve J. Liu
College Station, Texas
February 13, 1998
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The developmental work of this software for training in wavelet algorithms is based on our experience in teaching short courses for the
Texas Engineering Extension Services over the last few years. Our
gratitude goes to Professor Charles K. Chui for his organization of the
short courses, help in the review of this manuscript, and many valuable
suggestions. Some of the computer code evaluations and graphical illustrations were carried out by our former and current students, Howard
Choe, Hung-Ju Lee, Nai-wen Lin, Dongkyoo Shin, Hsien-Hsun Wu,
Michael Yu, Xiqiang Zhi. We thank them for their careful and detailed
work. Special thanks are due to Brandon Scott Wadsworth, who spent
countless hours perfecting the GUI of the software. We are indebted to
Margaret Chui for her help in the editorial work of this manuscript.
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Jesus Christ our Lord, and
our wives, Sophia and Grace.
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Part I
Wavelet Theory
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From Fourier Analysis to Wavelet Analysis
Before the work of Haar [1], a continuous function (or an analog signal)
f ( t ) was represented either by an entire series (polynomial basis) or
by a Fourier series (sinusoidal basis). Since both bases functions have
infinite time duration (—00 < t < oo), it is awkward to use any of
these bases to represent finite time-domain (transient) signals. Haar's
work demonstrated that a continuous function can be approximated
arbitrarily closely by an orthonormal basis with local support (the Haar
basis). Since then, mathematicians have constructed various new bases
to represent a variety of analog functions.
During the past three decades, signal processing has grown to become a major discipline in engineering and computing science. Since
most finite energy signals, either natural or otherwise, are transient (or
nonstationary) in nature, it is most natural and effective to represent
these signals by localized finite-energy bases. Because wavelets belong
to this class of bases, the development of wavelet theory and its applications to signal processing in various engineering disciplines has gained
tremendous popularity in recent years.
Fourier analysis has been the major mathematical tool for signal
representation and processing for at least the past 50 years. The discrete version of this approach, called Fourier series, breaks down a given
signal into sinusoidal functions with different harmonics of the fundamental frequency. Since sinusoidal signals are periodic signals, Fourier
analysis is an excellent tool for analyzing this class of signals. However, it is inefficient for representing transient signals. Recall that the
definition of the Fourier transform of an analog function f ( t ) is defined
It is easy to show that if f ( t ) is replaced by a delta function 6(t — to) at
t = to, then the Fourier transform (or spectrum) becomes a sinusoidal
function e-wto = costouj — j sin tow. Hence, it takes an infinite number
of frequencies to represent a signal that exists at only one point in the
time domain. On the other hand, if the function to be analyzed is
a sinusoidal function of a single frequency, the spectrum is a delta
function. A careful study of this consideration leads to discrete Fourier
analysis. Discrete Fourier analysis is very efficient for studying global
periodic function. However, for transient signals, such as music, image,
speech, acoustic noise, seismic signals, thunder, and lightning, it is
more efficient to represent these signals by localized finite energy bases.
Other drawbacks of the Fourier analysis include the following:
1. The Fourier transform can be computed for only one frequency at
a time.
2. Exact representations cannot be computed in real time.
3. The Fourier transform provides information only in the frequency
domain, but none in the time domain.
Short-Time Fourier Transform
Fourier analysis, being unable to provide localized time and frequency
information simultaneously, becomes the most serious drawback for processing of transient signals. In order to gain information from both time
and frequency domains simultaneously, engineers have used the ShortTime Fourier Transform (STFT) to accomplish their goal, even if only
The STFT is formally defined by the integral transform
where the overbar indicates complex conjugation. The function w(t)
is called the window function to be chosen by the user. This is the
reason that the STFT is also sometimes called the Windowed Fourier
Transform (WFT). The variables ( t , w ) are the transform variables
from the single time domain variable t. Hence, the STFT transforms
a one-variable function into a two-variable function. It provides timefrequency information of the function f ( t ) on the time-frequency (t-f)
plane. The complex spectrum of S T w f ( t , u j ) gives approximated spectral properties of the function near the time location t. It is only an
approximation, since the expression in (1.2) is the Fourier transform of
the product of the functions f ( t ) and w(T — t). In other words, (1.2)
gives the spectrum of the product function f(r}w(r — t) instead of the
function f ( T ) alone. Even if the window function w(t) is a rectangular window with unit amplitude and f ( T ) is a pure sinusoidal function
cos(wot)5 the transform of the product f ( t ) w ( T — t) is not just a delta
function in the spectrum. Rather it is the convolution of the delta
spectrum with the sine function spectrum of the rectangular function.
This broadening of the delta function dwj — wo) to a sine (a; — wo) function represents the effect of the truncation due to the window function.
We call that a windowing effect on the function /(T). We will see in
later sections that the accuracy of the information on the t-f plane is
controlled by the widths of the window in time and frequency domains.
In addition, the uncertainty principle governing the area of the window
in the t-f plane places a limit on the resolution that one can achieve
using STFT.
The spectral domain equivalent of (1.2) comes from the Paseval identity, which gives
Expression (1.3) indicates that the time-domain windowing process is
also a spectral-domain windowing process. It provides information on
the spectral energy of the signal near the center of the window.
The original signal f ( t ) can be recovered from the STFT uniquely.
Hence the STFT is a unique transformation. The recovery formula for
the STFT is given by
where \\w\\ is the norm of the function that will be defined in the next
Window Measures
In order to make an intelligent choice of a window function for the
STFT, some measures of "goodness" must be established to evaluate
the suitability of the window function. Engineering measurements such
as the main lobe zero-crossing and the 3db points do not gauge the
concentration of energy of the function. In the wavelet literature, the
RMS width of the function is used to measure energy concentration in
the time and frequency domains. These measurements are explicitly
expressed respectively as
Here, the usual definition of the energy norms
applies to these formulas. The measurement Aw is considered as half
of the time-domain window width, which measures the concentration
of energy in the window. A smaller window width means more energy
is concentrated in a smaller area. One may use a small time window
width to deduce the fine structure of a signal. On the other hand, Dw+
is the measurement of energy concentration in the spectral domain. In
addition, t* and w* are the centers of gravity of the window in the
time and spectral domains, respectively. If w(t) is real and even, and
the average value of the function is zero, then w(w) is also symmetric
with respect to the origin and w(0) = 0. In this case, the integration in
the frequency domain expression extends only from 0 to oo, and by the
definition of (1.8), a;* is located on the u;-axis other than the origin. In
general, one must compute the window widths numerically, since most
of the window functions we are considering in this manual may not be
described analytically.
We can easily see that the window area of the STFT in the t-f plane
is given by
The center of the window is located at (t + t*,w + w*) on the t-f
plane where t is the time parameter being considered. Once we have
chosen the window function w ( t ) , the window widths 2 Dw and 2 Dw
are fixed everywhere in the t-f plane since they are independent of t
and (jj. This effect is demonstrated on the t-f plane as given in Fig
1.1. This characteristic presents a distinct disadvantage of the STFT
since it creates difficulty in accurately processing signals with a timedependent spectrum, such as a chirp signal. In order to achieve a high
degree of accuracy, the STFT must be repeatedly applied to the signal
with a varying window width each time.
Figure 1.1: STFT windows
What Is a Wavelet?
Wavelets are finite-energy functions with localization properties that
can be used very efficiently to represent transient signals. Efficiency
here means only a small finite number of coefficients are needed to represent a complicated signal. In contrast with the sinusoidal functions of
infinite extent (big waves), "wavelet" implies a "a small wave." Strictly
speaking, it also means mathematically that the area under the graph
of the wavelet u ( t ) is zero, i.e., f-u(t)dt = 0. In the spectral domain, this property is equivalent to -0(0) = 0, i.e., the spectrum of the
wavelet has a value of zero at w = 0. In engineering terms, the wavelet
has no d-c offset. This spectral-domain behavior makes the wavelet a
bandpass filter. The reader can use the Toolware to generate the graph
of any wavelet listed in the Part III by following the instructions given
later in this manual.
It is easy to see from any wavelet and its spectrum that the energy
of the wavelet is concentrated in a certain region of both the t- and ujaxes. This localization property is an important feature of the wavelets.
If a wavelet is more localized (that is, the energy of the wavelet is
concentrated in a smaller region), it produces a better representation
of the signal in the time-frequency (or time-scale) plane. In our case,
"better" means higher resolution and requires less coefficients in the
D2 scaling function (scale=1)
D2 scaling function (scale=1/2)
D2 scaling function (scale=1/4)
Figure 1.2: The scaling functions of the Daubechies-2 wavelet at three different
1.4.1 Wavelets at different resolutions (scales)
For a given wavelet u ) ( t ) , a scaled and translated version is designated
The parameter a corresponds to the scale while b is the translation
parameter. The wavelet u01(t) = u ( t ) is called the basic wavelet (or
mother wavelet). The Daubechies D2 scaling function at three different
scales is shown in Fig 1.2.
It is important to note that the shape of the wavelet remains the
same under translation and scaling. We will show below that with the
proper choice of (6, a), the set of wavelets ba(t) constitutes the basis
functions of a Reisz (or stable) basis in the L2 (or finite-energy) space.
Keeping this concept of basis in mind, we will show that the idea of
wavelet signal processing is not much different from that of Fourier processing, at least when "orthonormal wavelet" are considered. Instead
of decomposing a signal into sinusoidal functions of different frequencies (the Fourier basis), wavelet signal processing seeks to decompose
a transient signal into a linear combination of a scaled and translated
version of the basic wavelet (the wavelet basis).
Continuous Wavelet Transform
The continuous (integral) wavelet transform (CWT/IWT) of a signal
x(t) is a linear transform defined by the integral
The last expression of (1.13) represents the inner product of two
functions, denned in the L2 space by
Referring to (1.13), the CWT computes, via the inner product formula,
the wavelet coefficient of x(t) associated with the wavelet u b a (t). This
coefficient indicates the correlation between the function x ( t ) to the
wavelet u b a (t). Higher correlation produces a larger coefficient. An
example of the CWT is shown in Fig 1.3.
For the sake of comparison, we write the STFT in the inner product
form as
One immediately sees from (1.13) and (1.15) that the difference between CWT and STFT is that the kernels for these two transforms are
different. CWT and STFT are interchangeable if we switch
Figure 1.3: The graphical display of the CWT coefficients of a voice signal
We remark here that the basis function w(t — b)ej wt is a sinusoidal
function at a single frequency modulated by the window function w(t).
The window widths are fixed independent of b and u. Based on the
window measures discussed earlier in this manual, the window area in
the t-f plane for the wavelet at a given scale a is given by
We see clearly that the window area of the wavelet remains the same,
namely, 4DwDw. However the time and spectral window widths are
dependent on the scale a. (See Fig 1.4.) If a becomes large (i.e., at
low frequency), the window width in the time domain is large while
the window width in the spectral domain becomes small. This is the
case in processing signals in fine resolution in the spectral domain while
the time-domain resolution is coarse. For processing of a signal at low
frequency, one should use wavelets with a large scale.
Although there are striking similarities between the STFT and the
CWT, their results in signal processing are quite different. One must
compute the STFT of a broadband signal on the t-f plane for several
sizes of the same window to capture the high- and low-frequency characteristics of the signal. However, one only needs to compute the CWT
once to detect both the high- and low-frequency events in the signal.
Figure 1.4: Window widths for wavelets
The original signal is uniquely recovered by the double integral
where C$ is a finite constant given by the integral
Multiresolution Analysis
We have shown that the CWT has a unique advantage because its window widths can be controlled by the scale parameter a. However, we
also see that the computation load of the CWT is quite heavy in order
to capture all the characteristics of the signal. To alleviate this computational burden, mathematicians have developed the Discrete Wavelet
Transform (DWT) to minimize the redundancies existing in CWT. Although the algorithm of DWT is identical to that of the two-channel
filter bank analysis, the underlying meanings of these algorithms are
different. We will point out these differences in later sections.
The most important feature of multiresolution analysis (MRA) MRA
is the ability to separate a signal into many components at different
scales (or resolutions). For a specific choice of the scaling parameter
(such as a = 2J, j E Z), the decomposition algorithm is equivalent to
putting signal components into successive frequency octaves. Similar to
multiband signal decomposition, the goal here is to apply the "divide
and conquer" strategy on the signal so that individual components may
be processed by different algorithms. We present here the essence of
the MRA by considering the properties of the approximation subspaces
and the wavelet subspaces.
Approximation subspaces
A function (0(t) L2, called a scaling function (or an approximate
function), generates a nested sequence of subspaces (approximation
subspaces) {Vj} of L2 such that
and satisfies a dilation (refinement) equation
for some a > 0, a
1, and some coefficient sequence {pk} E I2. For
the DWT to be discussed in some detail later, we will consider a = 2
that corresponds to a dyadic octave scale in the spectral domain. More
precisely, V0 is generated by {0 — k) : k E Z}. In general, Vn is
generated by {0(2n • —k) : k E Z}. The symbol (•) stands for the
dummy variable.
Wavelet subspaces
Let the subspace Vn constitute an orthogonal sum (for which we use
the symbol 0)of mutually orthogonal subspaces Wj of L2 as
The subspace Wn (the wavelet subspace) is said to be the orthogonal complementary subspace of Vn in Vn+i. This relationship is best
Figure 1.5: Approximation subspaces and wavelet subspaces
described by Fig 1.5. We should take note of the following two observations:
1. Subspaces {Vj} are nested.
2. Subspaces {Wj} are mutually orthogonal.
Mathematically, they are expressed by
The subspaces {Wn} are generated by some function u(t) called a
"wavelet," in the same way as {Vn} are generated by 0 ( t ) . In other
words, any function fj(t) £ Vj can be written as
and any function gj(t) E Wj can be written as
for some coefficient sets {cj,k}
adopted the notation
in l2. Here we have
We may restate here that a function f ( t ) can be decomposed into many
components such that
where the function fM(T) is the approximation of /(t) from the space
VM- For example, if a signal is sampled at the rate of 256 samples per
second, we may represent the signal by
This signal can also be represented by
Two-scale relations
The equation in (1.21) expresses the relation of a basis function between
two different scales. The two-scale relation formalizes this relationship
mathematically. Since
we can write (0(t) and u ( t ) in terms of the bases that generate V1. In
other words, there exist two sequences {pk} and {qk} E L2 such that
In general, for any j 6 Z, the relationships between Vj,Wj
are governed by the two-scale relations
Figure 1.6: Daubechies D2 scaling function
These formulas of the basis function sets {0(2jt)} and {u(2 J 't)} are
built up by a linear combination of o(2 J+1t ). For a given {pk} sequence,
the relations (1.36) and (1.37) can be graphically describable. We use
the scaling functions of Daubechies (D2) and the cubic spline as examples. The two scale relation is graphically displayed in Fig 1.6 and Fig
Decomposition relation
The relations (1.36) and (1.37) are often referred to as the reconstructive relations. The decomposition relation is for separating a signal into
components at different scales. The subspace relation in an MRA
indicates that there exists a relation between the basis functions in these
subspaces. Since o(2t) and o(2t — 1) are two distinct basis functions in
Figure 1.7: Cubic B-spline
j+i, there exist two sequences {ak} and {bk} in I2 such that
Writing these relations in their generalized form, we have
for all l E Z. For an arbitrary resolution level j, this relation becomes
There exists a definite relationship between the two-scale sequences
{pk},{qk} and the decomposition sequences {ak},{bk}. We will consider these relations when different types of wavelets are discussed.
Development of the decomposition and reconstruction
The decomposition (analysis) and reconstruction (synthesis) algorithms
are the most often-used algorithms in wavelet signal processing. As
mentioned earlier, the idea consists of simply dividing the signal into
components so that each component may be processed with a different algorithm. The important issue of this algorithm is to be able to
reconstruct the signal perfectly if we apply all-pass filters on all signal
components. These algorithms are based on the two-scale relations and
the decomposition relation as developed in the previous sections. We
rewrite these relations here for convenience.
Since the MRA requires that
we have
We substitute the decomposition relation (1.40) in (1.46) and interchange the order of summations. Comparing the coefficients of (f)j,k(t)
and U7,j,k(t), we obtain
where the right sides of (1.47) and (1.48) correspond to a down-sample
after convolution. These formulas relate the coefficients of the scaling
function from a given scale to the scaling function and wavelet coefficients at twice the given scale.
Figure 1.8: Decomposition and reconstruction
The reconstruction algorithm is based on the two-scale relations.
When we sum the functions at the jth resolution, we have
We substitute the two-scale relations into (1.49) to yield
Again, we compare the coefficents of O j + i k ( t ) on both sides of (1.50)
to yield
where the right sides of the equations correspond to an up-sampling
before convolution. We use a block diagram (Fig 1.8) to illustrate the
procedure of these algorithms.
Mapping of functions into the approximation space
Before we go on any further on the extensions of the decomposition
and reconstruction algorithms, we first need to show how to obtain the
scaling function representation of an input data. That is, for a given
input signal s ( t ) , we wish to find the representation
are the input scaling function coefficients to be processed.
Many users of wavelets have used the sample (discretized) values of
s(t = £) as the input. We want to stress the fact that in general,
If we require s(k/2j) = Sj(k/2j), then the two sides of (1.53) are equal to
each other only if the scaling function is interpolatory, such as firstorder B-spline that corresponds to the Haar wavelet and the linear Bspline. It is therefore advisable to obtain cj,k from the sample data s(t =
k/2j) before applying the decomposition algorithm. In the following, we
consider the orthogonal projection of s(t) onto the Vj space.
Assuming Vj is a subspace of L2 and s(t) E L2, we wish to find Sj(t)
E Vj as an approximation to s ( t ) . Write
and consider Sj(t) to be the orthogonal projection of s(i) onto the
subspace. Then, s(t) — Sj(t) is orthogonal to Vj so that
In matrix form this equation becomes
is the autocorrection sequence of the scaling function. If the scaling
function is compactly supported, the corresponding autocorrelation matrix in (1.57) is banded with a finite number of diagonal bands. Also,
if the scaling function forms an orthonormal basis, then
If we have only the sample values of the signal s
to approximate the integral in (1.56) by a sum
s(t — k/2j), we have
This demonstrates the difference between the coefficients of the scaling function series and the sample values of the signal. The former
is used in wavelet signal processing (analog processing in the time domain, since the coefficient sequence of the scaling function series and
the wavelet series are to be processed), while the latter is used in filter
bank processing (digital processing in the frequency domain).
Types of Wavelets
In the preceding discussions of the approximate subspace and wavelet
subspace, the basis functions that span the subspace can have orthonormal, semi-orthogonal, or biorthogonal bases. There are conditions to be
satisfied by each type of basis. We will state these conditions separately
as follows.
Orthonormal wavelet bases
Orthogonality of functions depends on the definition of the inner product in that vector space. We have used the inner product defined in
the L2 space. Two different functions are orthogonal to each other if
the inner product of these functions is zero. That is,
If we take g(x) = /(x), and
we say that the function f ( x ) is normalized. Hence, if the set of basis
, 0j ' E Z spans an approximation space Vj and satisfies
the condition
it is a normalized orthogonal set, or, for short, an orthonormal set.
This definition applies to the wavelet subspace as well. A wavelet is
said to be orthonormal if the set of basis functions uj,k = 2j/ 2 u(2-7:r — k)
Since the wavelet subspace and the approximation subspace are orthogonal, we have
Notice that the relationships in (1.63) to (1.65) are all referred to the
same level of resolution. The relationships between wavelets at different
resolution levels are also orthogonal. That is,
Examples of the orthonormal wavelet basis include the Haar wavelets,
the Daubechies orthonormal wavelet bases of all orders, the Meyer
wavelets of all orders, and Battle-Lemarie orthonormal spline wavelets
(not compactly supported). We note here that both Meyer and BattleLamarie wavelets are not compactly supported (finite time duration).
Decomposition and resconstruction sequences
The relations between the decomposition sequences {a^} , {b^} and the
reconstruction sequences {pk} , {q^} are the simplest for an orthonormal
basis. For perfect reconstruction of the signal, we may use the following
Once the pk sequence has been found through the construction of the
two-scale relation of the scaling function, the other sequences are determined.
Semi-orthogonal wavelet bases
In the case of cardinal B-spline functions of orders higher than 1 and
their corresponding compactly supported spline wavelets, the integer
translates of the scaling functions, i.e., B-splines, as well as the Bwavelets are not orthogonal. That is,
However, the orthogonality between the scaling function Nm and the
B-wavelet U>N m , as well as translates of wavelets at different resolutions,
is still preserved. In this case, the compactly supported spline wavelet
is called a semi-orthogonal wavelet. In order to facilitate efficient computations of the expansion coefficients in the approximation spaces or
the wavelet spaces, a dual scaling function 0 and a dual wavelet Ui can
be found to satisfy the biorthogonality relation
where 0 = Nm and u = u N m have been used in these two equations.
We remark here that both the dual scaling function (the dual B-spline)
and the dual spline wavelet are both functions in the spline space of the
same order. Hence, these duals can be expanded into B-spline series of
the same spline order. We will show in later sections how to use these
relations to map coefficients between wavelet spaces and dual wavelet
Decomposition and reconstruction sequences
The sequences for the two-scale relations in the mth order spline space
The two-scale relations for the dual spline and dual wavelets are slightly
more complicated. Since the B-spline functions of order m > 2 are not
orthogonal basis, the sequences {ak} , {bk} comes from the dual spline
and dual wavelets which are not compactly supported, but they are
both infinite sequences with exponential decay. Explicit expressions for
these sequences are
where the z-transform of {gh} is the reciprocal of the z-transform of the
Euler-Frobenius-Laurent Polynomial E0(z), namely
Numerical values for these sequences are listed in Table 5.5 of the book
Wavelets: A Mathematical Tool for Signal Analysis(SIAM Publ., 1997)
for m = 2 (linear splines) and m = 4 (cubic splines).
Biorthogonal wavelet bases
It is known in the signal processing literature that finite impulse response (FIR) filters with symmetric or antisymmetric coefficients have
linear phase or psuedo-linear phase characteristics. Linear phase properties are important for minimizing signal distortion during processing.
The processing sequences (or the FIR filters) {pk} and {qk} of finitely
supported orthogonal wavelets are generally not symmetric. In order
to take advantage of the symmetric property, one has to give up orthogonality. Filter designs in a two-channel filter banks are examples
of a biorthogonal basis. In this case, orthogonality between filters is
not required. The only requirement for filter bank design is that the
output of the filter bank be a delayed version of the input. That is, the
output signal must be a perfect copy (perfect reconstruction condition)
of the input signal. Biorthogonal wavelets of Cohen, Daubechies, and
Feauvears are examples of this class of bases. In addition, there may
not exist a scaling function or wavelet associated with filters designed
for filter bank processing. In other words, there may not be a nested
V spaces for arbitrary FIR filters as their two-scale sequences. For
this reason, the two-channel filter bank is an excellent alternative for
processing digital signals.
Decomposition and reconstruction sequences
The two-scale sequences of two biorthogonal wavelets of Daubechies
et al. are listed here as a reference. For more details of biorthogonal
wavelets, readers are referred to Section 5.6 (particularly, Example 5.41)
of the accompanying text An Introduction to Wavelets [3]; the text
Wavelets: A Mathematical Tool for Signal Analysis, Chapter 5, by
Chui [2]; and the engineering texts, Wavelets and Subband Coding by
Vetterli and Kovacevic, Prentice Hall, 1995 [4], and Wavelets and Filter
Banks by Strang and Nguyen, Wellesley-Cambridge Press, 1996 [5].
Coefficients of the B-97 Daubechies biorthogonal wavelet are given in
the following table.
Wavelet Packets
The hierarchical wavelet decomposition produces signal components
whose spectra reside in consecutive octave bands. In certain applications, the spectral resolution produced by this decomposition may not
be fine enough. One may want to use the CWT to obtain the necessary finer resolutions by changing the scale parameter a by a small
increment. However, this increases the compuation load by orders of
magnitude. In order to avoid this problem, wavelet packets are generalizations of wavelets in that each octave frequency band is itself further
subdivided into finer frequency bands by wavelet packet transforms.
In other words, the development of wavelet packets is a refinement of
wavelets in the freqency domain and is based on a mathematical theorem proven by I. Daubechies (the ) splitting trick). The theorem is
stated as follows:
If /(• — k) k^z forms an orthonormal basis and
then {F1(- — 2/c),F 2 (. — 2 k ) ; k E Z} is an orthonormal basis for E =
span{f(- — n); n E Z}. The proof of this theorm is given in her book
Ten Lectures on Wavelets (SIAM PubL, 1992, p. 326) [6].
This theorem is obviously true when / is the scaling function 0,
since the two-scale relations for 0 and the wavelet u give
If we apply this theorem to the Wj spaces, we generate the wavelet
packet subspaces. The general recursive formulas for wavelet packet
generation are
where m,0 = 0 and mi = u are the scaling function and the wavelet,
respectively. For L = 1, we have the wavelet packets m2 and m3 generated by the wavelet m1 = u. This process is repeated so that many
wavelet packets can be generated from the two-scale relations. Because
of the many components available, any given signal can be represented
by a choice of wavelet packets at different levels of resolution. An optimized algorithm can be constructed to maximize a certain measure
(such as energy or entropy). Best-bases and best-level are two popular
algorithms for signal representations. The reader can find these algorithms in [7]. The Toolware is designed so that the decomposition and
reconstruction algorithms can use either wavelets or wavelet packets.
Wavelet packet algorithms
The wavelet packet algorithm follows the wavelet algorithm with the
extension that the wavelet coefficients djk are also being processed in
the same way as the scaling function coefficients cJ,k• Hence, for every
decomposition process, the number of components is double that of the
previous resolution. Suppose an input signal s(n) has been mapped
into the approximation space Vj. That is, s (n) <— > cj,k. Using the decomposition sequences an and bn, we obtain the scaling function series
coefficients Cj-1,k and the wavelet series coefficients dj-i,k. We can represent the scaling function component and the wavelet component at
Figure 1.9: Wavelet packet decomposition tree
The coefficient sequences Cj-1,k and dj-1,k
and bn into four sequences
decomposed through ar
in which the last two sequences m_2,k, and Vj-2,k are wavelet packet
series coefficients. This process can be repeated for the four sequences
in (1.77) to produce eight coefficient sequences corresponding to eight
wavelet packet components of the original signal s(n). The decomposition tree structure is given in Fig 1.9. The algorithm for WP decomposition and reconstruction of a one-dimensional signal is shown in Fig
Two-Dimensional Wavelets
When the input signal is two-dimensional (2-D), it is necessary to represent the signal components by two-dimensional wavelets and twodimensional scaling functions. For any scaling function 0 with its corresponding wavelet u;, there are three different 2-D wavelets and one
2-D scaling function using the tensor-product approach. We can write
Figure 1.10: Block diagram for the WP decomposition of a signal
the 2-D wavelets as
and the 2-D scaling function as
In the spectral domain, each of the wavelets and the scaling function
occupies a different portion of the 2-D spectral plane. When we analyze
a 2-D signal, the decomposition algorithm generates four components
from the input signal. With respect to the spectral domain, the components are labeled low-high (LH), high-low (HL), and high-high (HH),
corresponding to the wavelets
(x,y), M = 1,2,3. The component
that corresponds to the scaling function signal is called the low-low
(LL) component. The terms low and high refer to whether the processing filter is lowpass or highpass. The decomposition of a 2-D signal
results in what is known as a hierarchacal pyramid. Because of the
downsampling operation, each image is decomposed into four subimages. The size of each subimage is only a quarter of that of the original
image. Readers can refer to the last section of this manual to try to
decompose a 2-D image signal and see the resultant component display.
Two-dimensional wavelet packets
Two-dimensional wavelet packets are refinements of 2-D wavelets just
as in the 1-D case. Not only is the LL component (the scaling function
component) decomposed to obtain further details of the image, but the
other wavelet components (LH, HL, HH) can also be further decomposed. For example, starting with an original image with size 128x128,
a 2-D wavelet decomposition of this image will result in four subimages
of size 64x64. Continuing the decomposition, one gets 16 2-D wavelet
packet subimages of size 32x32. The computational algorithm for 2D wavelet packets is no different than that for the 2-D wavelets. It
requires an orderly bookkeeping for keeping track of the directions (x
or y), the filters used (highpass or lowpass), and the resolutions. The
reader can make use of this wavelet Toolware to practice the wavelet
packet decomposition and reconstruction of 2-D signals.
Part II
Wavelet Algorithms
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Wavelet Algorithms Overview
This part discusses the approach we take to numerically implement
the mathematical statements given in Part I. In addition, we also include several algorithms that have been used in many wavelet signal
processing applications such as thresholding, noise removal, and signal
compression. The algorithms are written in procedural form so that
the reader can easily follow them to build codes using his/her preferred
computer languages. The following algorithms are included in this section:
1. Computation of STFT for 1-D signal
2. Computation of CWT for 1-D signal
3. Algorithm for generation of the graph of scaling functions
4. Algorithm for generation of the graph of wavelets
5. Mapping between B-splines and their duals
6. Decomposition of 1-D signals
7. Reconstruction of 1-D signals
8. Thresholding
9. Two-dimensional extension of the wavelet algorithms
10. Other algorithms
Algorithm for Computing the STFT
Let us rewrite the definition of STFT here for easy reference:
A straightforward approach to computing the STFT is to make use of
the Fast Fourier Transform (FFT) algorithm that can be found in many
numerical analysis books. The procedure for computing the STFT is
given here:
1. Choose a symmetric window function and sample it at the same
rate as the input data.
2. Count the total number of samples of the window function, say N.
3. To take care of the edge (or boundary) effect of the algorithm, we
may reflect N/2 (if N is even) or N-1/2 (if N is odd) data points to
the outside the edges of the 1-D data set at both ends of the set.
That is, if the data set has M data points, the modified data set
will have a total of M + N points from - y to M + y .
4. Position the center of the window sample data (or the center of the
window) at n = 0 and multiply the window sample with the data
set. Take an TV-point FFT to obtain the spectrum of the function
at n = 0.
5. Shift the window data to the right by one point and repeat step 4
to obtain the spectrum of the function at n = 1.
6. Repeat step 5 until the center of the window has reached the end
point of the data set at n = M — 1. The resulting output data
should be a rectangular array of M x N points.
7. Display the data through either a 3-D plot or a gray-scale plot.
We use an example similar to the one given in Dauchies' book for
illustration. Let the function to be processed be
f ( t ) = sin 10007t + sin 2000t + a[6(t - t1) + (t -
t 2 )],
where a is a constant chosen to be 3 and t1 and t2 are 0.192 and 0.196
msec, respectively. The function is sampled at 8000 points per second.
The frequencies are chosen so that when the time window width is small
enough to resolve the delta functions, the corresponding frequency window width is too wide to resolve the two frequencies accurately. We
use a rectangular window of different window widths, 8 and 64 points,
respectively. The STFTs of the functions for these windows are computed using the Toolware, and the results are shown in Fig 2.1 and Fig
2.2. One can easily see from these graphs that if the window in the
time-domain is wide enough to resolve the low frequencies, the delta
functions are not resolvable. If the time-domain window is narrow
enough to resolve the delta functions, the frequency resolution is low.
We will show later that the CWT can resolve the delta functions and
the frequencies simultaneously in only one pass by varying the scale
Figure 2.1: STFT outputs for the window size equal 8
Algorithm for Computing the CWT
The definition of the CWT given in part A is written here:
where h(t) =
is used in (2.83). Using the convolution integral
expression, (2.83) can be computed by using FFT as in the case of
STFT. The steps are outlined as follows:
1. Select a wavelet of your choice to be used for this computation.
2. For a given scale a, sample the wavelet at the same rate as the
input data.
3. Treat the data at the edge the same way as indicated in the STFT.
4. Compute the FFT of the input data and that of the wavelet at
scale a.
5. Multiply the two FFTs and take the IFFT to obtain the CWT of
the function at the scale a.
Figure 2.2: STFT outputs for the window size equal 64
6. Repeat steps 2 through 5 for as many values of a as the user wishes.
7. Display data using a 3-D plot or gray-level display.
The same function used to demonstrate the STFT is also used here
to demonstrate the characteristics of the CWT. We choose the Morlet
wavelet and compute the CWT via FFT. The result shown in Fig 2.3
clearly shows the advantage of a flexible window of the CWT. It requires
only one computation to resolve both the frequencies and the delta
functions in (2.82). One can compare this graph of the CWT to that
of the STFT.
Computation and Display of Scaling Functions
We give two algorithms for computing the scaling functions.
Spectral-domain method
Let us recall the two-scale relations for the scaling function
Figure 2.3: CWT outputs
and consider the spectral domain expression of this relation, namely,
where P(*) is the z-transform of the sequence pk. Hence, the scaling
function <0>(t) can be obtained by finding the IFFT of the infinite product given by (2.84). In general, the infinite product must be truncated
so that there is only a finite number of points in the IFFT program.
This approach is not easy to implement because the complexity of
the product of the discrete Fourier transform of the sequence {pk}- If
the number of elements in this sequence is small, the problem is still
manageable; otherwise, the data storage required is substantial. The
following alternative approach can be used.
Iterative method
We modify the two-scale equation to fit an iterative technique by considering
The index n indicates the number of iterative loops. For initialization, the user may use either first- or second-order B-splines. The
iteration should converge after 5 to 10 iterations if the regularity of
the function is high. However, for some sequences designed by the perfect reconstruction filter bank approach, the algorithm may not even
converge at all. In other words, there are filter banks that are not associated with scaling functions and wavelets. The final data may be
seen by using the usual 1-D graphic display. The code to generate the
scaling function in this Toolware is based on this algorithm. We outline
the procedure as follows:
1. Initialize the program by setting all data files to zero.
2. Set desired number of iteration, say 10, and set the iteration index
to 1.
3. Input the initial trial function ( 0 0 ( X ) . One may use an impulse
function, a rectangular pulse (i.e., first-order B-spline), a triangular pulse (i.e., second-order B-spline), etc.
4. Carry out (3.13) by convolving the function with the Pk sequence.
5. Upsample the resulting sequence by inserting zeros in between
every other data point. This sequence is 01(x).
6. Increase the iteration index by 1 and repeat steps 4 to 6 until 10
iteration cycles have been completed.
The reader may use the Toolware to familiarize himself/herself with
scaling functions included in the software.
Computation and Graphical Display of Wavelets
To generate a wavelet that satisfies the two-scale relation
we can use either the spectral or the time-domain approach. For the
spectral-domain approach, we simply multiply the infinite product in
(2.84), except that the index k runs from 2 to
z-transform of {qk} , namely,
, by one-half of the
For the time-domain approach, we take the linear combination of the
translate scaling function 0(2t — k) using the coefficients qk. We use
the time-domain approach in this Toolware to generate the graphs of
different wavelets. The reader should use the Toolware to view different
wavelets as given in Part C.
Mapping between B-Splines and Their Duals
When B-splines are chosen to represent a signal s ( t ) , we have shown
in Part I that it is necessary to map the sample values s
an approximation of the signal Sj(t) =
This procedure
maps the signal into the B-spline (or the approximation) space at the
jth resolution. Since the decomposition sequences {ak} and {bk} for
B-splines and B-spline wavelets are infinitely long, a truncation of the
sequence is necessary with a reasonable filter length to ensure adequate
accuracy. For processing of large images (e.g., mammograms at 4000 x
5000 x 12 bits per image) or 3-D images (e.g., multislice CAT scans),
the decomposition process is inefficient and the compuational load is
large. Since the B-splines N m (2jt — k) and its duals
the same subspace Vj, it is convenient and efficient to map the B-spline
coefficients cj,k to the dual-spline coefficients
After that, we may
process the signal using sequences {pk} and {qk} , which are finite with
relatively short filter lengths.
be a signal approximated by a B-spline series
and the dual B-spline series
, respectively, ck,j
are the spline coefficients that have been determined through the initial
mapping from the sampled values. To obtain the dual B-spline coefficients, one simply makes use of the orthogonality condition between
the B-spline and its dual,
Let j = 0 by making the sampling interval unity. We compute the dual
coefficients ck,j by
Since B-splines have compact support, the autocorrelation sequence
is a finite sequence where
so that
can be computed very efficiently. If the final objective of the processing
requires the reconstruction of the signal, as in image compression, one
needs to eventually convert the processed coefficients back to B-spline
coefficients for image quality evaluation and display. However, if the
processing objective is for detection and recognition, the dual-spline
coefficients can be used for neural network training purposes.
Decomposition of a 1-D Signal
The decomposition algorithm for scaling function coefficients is described by
We can rewrite the expression as
which is interpreted as convolution before downsampling by 2. In terms
of digital signal processing symbolism, it is given in Fig 1.8.
The computation of the wavelet coefficients at one level of resolution
is carried out in a similar manner, namely,
Comine these two steps to form the decomposition block as shown later.
This decomposition block can be repeated and sequentially applied to
the scaling function coefficients Cj_p,k, to yield the wavelet coefficient
sequences dj-p-1,k,p = 0,1, 2,...., M.
Implementation of (2.94) is simple. Taking a scaling coefficient set
to convolve with the coefficient set {ak} and downsampling yields the
scaling function coefficients at one lower resolution level. Repeating
the same procedure with coefficient set {bk} yields the wavelet coefficients. These procedures are repeated to yield the coefficients at lower
resolution levels.
Reconstruction of 1-D Signals
The two-scale relations for the approximation space and the wavelet
space constitute the reconstruction algorithm. The scaling function coefficients at a higher resolution level are computed by using the formula
Each summation can be interpreted as a convolution process after upsampling. This process is depicted in Fig 1.8. It can be repeated for
coefficient sequences {CJ- P , k } and {dj- p , k ,p = M,M — 1,...,0. The
reader should use the Toolware to practice this algorithm for 1-D signals.
Thresholding is one of the most often used processing tools in wavelet
signal processing. It is used in noise reduction, in signal and image
compression, and sometimes in signal recognition. The four types of
thresholding we use are (l)hard thresholding, (2)soft thresholding,
(3)quantile thresholding, and (4)universal thresholding. The choice of
thresholding methods depends on the application. We discuss each type
here briefly.
Hard thresholding
Hard thresholding sometimes is called gating. If a signal (or a coefficient) value is below a preset value, it is set to zero. That is,
where a is the threshold value or the gate value.
Soft thresholding
Soft thresholding is defined as
The function f ( x ) generally is a linear function (a straight line with
slope to be chosen). However, spline curves of third or fourth orders
may be used to effectively weight the value greater than a.
In some applications such as signal compression, using a quadratic
spline curve may increase the compression ratio by a certain amount.
Quantile thresholding
In certain applications, such as image compression, where a bit quota
has been assigned to the compressed file, it is more advantageous to set
a certain percentage of wavelet coefficients to zero to satisfy the quota
requirement. In this case, the setting of the threshold value a is based
on the histogram and total number of coefficients. The thresholding
rule is the same as hard thresholding.
Universal thresholding
In some noise removal applications in which the noise statistics is
known, it may be more effective to set the threshold value based on
the noise statistics. For example, Donoho and Johnstone [8] set the
threshold value to be
where v is the standard deviation of the noise and l is the cardinality
of the data set. This threshold value can be used in either hard or soft
thresholding as shown earlier.
Implementations of the hard, quantile and universal thresholding methods are quite simple. One simply subtracts the threshold value from
the magnitude of each coefficient. If the difference is negative, the coefficient is set to zero. If the difference is positive, no change is applied
to the coefficient. To implement the soft thresholding by using a linear
function of unit slope, the thresholding rule is
Two-Dimensional Extension of Wavelet Algorithms
We have mentioned in previous sections that the 2-D wavelets are tensor
products of the 1-D scaling function and the wavelet. Corresponding
to the scaling function and the wavelet in one dimension are three
2-D wavelets and one 2-D scaling function at each level of resolution.
As a result, the 2-D extension of the wavelet algorithms is the 1-D
algorithm applied to both the x and y directions of the 2-D signal. Let
us consider a 2-D signal as a rectangular matrix of signal values. In
the case where the 2-D signal is an image, we call these signal values
Pixel values corresponding to the intensity of the optical reflection.
Consider the input signal c j (m, n) as an N x TV square matrix . We may
process the signal along the x direction first. That is, we decompose the
signal row-wise for every row using the 1-D decomposition algorithm.
Because of the downsampling operation, the two resultant matrices are
rectangular, and of size N x ~. These matrices are then transposed and
processed row-wise again to obtain four ~ x y square matrices, namely,
cj-1 (m, n), di'1 (m, n), d J 2 - l (m, n), and d{- 1 (m, n). The subscripts of the
d matrices correspond to the three different wavelets. This procedure
can be repeated an arbitrary number of times to the c £ (m, n) matrix
(or the LL component), and the total number of coefficients after the
decomposition is always equal to the initial input coefficient TV2. The
reader may use the Toolware to practice the 2D decomposition and
reconstruction algorithms.
If the coefficients are not processed, the original data can be recovered exactly through the reconstruction algorithm. The procedure is
simply the reverse of the decomposition, except that the sequences are
{Pki Qk} instead of {ak, bk}. Care should be taken to remember upsampling first before convolution with the processing sequences.
Other Algorithms
Toolware also includes following codes for wavelet processing:
• Noise removal using hard thresholding
• Image compression using thresholding
• 1-D wavelet packet decomposition and reconstruction
• 2-D wavelet packet decomposition and reconstruction
Implementation of noise removal is straightforward and is not elaborated here. The user can make use of the Toolware to see the effect of
speckle noise being removed from an image, using hard thresholding.
Setting the wavelet (or wavelet packet) coefficients at small amplitudes to zero by thresholding compresses the image information because
it takes many fewer bits to represent clusters of zero coefficients. The
reconstructed image will lose fidelity because some high frequency components of the image have been removed by thresholding.
The algorithmic logic of the 1-D and 2-D wavelet packet algorithms
has been discussed in Part I. The basic algorithm is no different from
the wavelet algorithms, except the user has to keep track of the decomposition structure. A procedural discussion of these algorithms is
very lengthy and will not be presented here. We encourage the user to
practice this algorithm in the Toolware using the images provided, or
images from other sources.
Part III
Toolware User's Manual
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The Wavelet Toolware Overview
Wavelet Toolware is a 32-bit, stand-alone software program with all
necessary library routines bundled together in a package. It has five
tools: the one- and two-dimensional (1-D/2-D) discrete wavelet transform (DWT) tools, the continuous wavelet transform (CWT) tool, the
short time Fourier transform (STFT) tool, and the wavelet generation
tool. 1-D/2-D DWT tools respectively support DWT decomposition,
DWT coefficient processing, and DWT reconstruction of one- and twodimensional signals. The CWT tool reads in a 1-D signal data file,
computes its CWT coefficients, and then displays the coefficients on
the screen. Similarly, the STFT tool reads in a 1-D signal and generates the STFT coefficients for display. The wavelet generation tool is a
simple utility to generate B-spline wavelets and Daubechies orthonormal wavelets.
The 1-D and 2-D DWT tools have a similar operating structure.
They assist the user to decompose a signal, using scaled wavelets at
different levels, process DWT coefficients, and then reconstruct the
signal in a very flexible manner. A decomposed signal can be reconstructed from any level to any other lower level, with only seleced levels
affected. In addition to some simple processing functions already included in Toolware, the user can also add new functions to Toolware
through a defined dll program interface. Toolware creates new files only
at the explicit request of the user. Thus, accidental crash or termination
of Toolware execution should not affect existing files. Toolware runs
on the Microsoft Win95 or NT (3.1, 3.5, 3.51, 4) operating system.
For performance and stability reasons, we recommend systems with a
Pentium 133Mhz or equivalent processor, and 32MB of main memory.
The memory size should proportionally increase for processing of large
files to keep intermediate results.
3.1.1 Installation of Toolware
Toolware installation is very simple. The user just follows these steps,
and the installation program does the rest.
1. Insert the CD-ROM into the CD drive E, which is the most typical
CD drive label. If the label of the CD drive on your machine is
not E, use the proper CD drive label in subsequent steps.
2. Click on the run icon on the desktop.
3. Type in E:setup command, and the installation will start.
4. Interact with the installation software to load Toolware program
files and data files into the directory that you want to host Toolware.
Toolware supports several different wavelets, including Daubechies orthonormal wavelets (Daubl to DaublO); linear B-spline wavelet and
cubic B-spline wavelet; Battle-Lemarie spline wavelets; zeroth- and
first-order Meyer wavelets; and biorthogonal Daubechies wavelets (1,1)
to (1,3), (2,1) to (2,4), (3,1) to (3,5), (4,1), (4,2), and (5,1). As shown
in Fig 3.1, an image is decomposed using the tensor product of the
1-D transform of an image along its vertical and horizontal directions,
respectively. Boundary data for both 1-D and 2-D data are treated by
a simple wrap-around or data-reflection method to achieve perfect (or
nearly perfect) reconstruction.
Figure 3.1: The one-lelvel DWT decomposition structure of a 2-D image in Toolware
Toolware is not designed for solving high-precision computational
problems, but is meant for general learning and experimentation. Typical numerical errors in the decomposition and reconstruction of a
512 x 512 gray-scale image are listed in the following table. The signalerror ratio is defined as the absolute ratio of the reconstruction errors
divided by a signal, expressed in decibels. Our test results show that
the difference in numerical errors on different machines is very small.
signal-error ratio
2.39009e+02 dB
2.26000e+02 dB
Linear BSpline
Cubic BSpline
signal-error ratio
2.39108e+02 dB
2.37523e+02 dB
2.37418e+02 dB
2.37440e+02 dB
2.31849e+02 dB
2.39241e+02 dB
2.28870e+02 dB
1.68857e+02 dB
5.09022e+01 dB
1.68857e+02 dB
2.41220e+02 dB
2.57555e+02 dB
2.41210e+02 dB
1.19127e+02 dB
5.32399e+01 dB
2.17692e+02 dB
6.01285e+01 dB
6.14814e+01 dB
Using Toolware
Toolware is activated by double-clicking on the Toolware group icon,
Fig 3.2, which is placed at the designed directory during installation,
to bring out the main menu, Fig 3.3. After the main menu appears on
the screen, the user can activate any of the five tools simultaneously.
The five tools include one-dimensional discrete wavelet transform and
two-dimensional discrete wavelet transform and
continuous wavelet transform
short time Fourier transform (STFT)
and the generation of
Figure 3.2: The start-up icon for Tool ware programs
Figure 3.3: Tool ware main menu
These tools operate independently, and they use
different data formats.
2-D DWT Tool
After you open the 2-D DWT tool, you need to choose a data file before
proceeding with other processing functions. This is done by clicking on
3.3. 2-D DWT TOOL
Data file selection
Toolware accepts pgm image files, meaning that any files appended
with “.pgm” can be read into Toolware for processing. After the user
clicks on the File button, or its corresponding graphical icon, the file
selection window (Fig 3.4) will pop out for the user t o open an image
file for processing. From the file window, one can identify any image
file (in PGM format) in any directory and then load the file by clicking
on the
butt on.
Figure 3.4: File selection window for 2-D images
A loaded image is automatically displayed on the screen for DWT
and processing (see Fig 3.5 for a sculpture image, file name “art.pgm,”
taken from the main campus of Texas A&M University). The leftmost corner of this window lists the title of the tool and the chosen file name. The two buttons at the upper right corner (
maximize, minimize, and close the current window. The second row
DWT tool where each of these headings is a pull-down menu.
Although they could be activated from the pull-down menu, key
operations of the 2-D tool are represented by thumbnail icons at the
third row of its window.
The first group of four icons
enable opening, savi
ing a displayed image. The second group of three icons
the decomposition, reconstruction, and processing of the selected image. Up to five levels of DWT coefficients can be stored in the internal
buffer for display, but only levels 0 t o 3 can be processed by processing
e P button. By clicking on one of the following
), the user can display any particular decomposi-
Figure 3.5: A 2-D image in the 2-D tool
3.3. 2-D DWT TOOL
tion level of wavelet coefficients.
All DWT coefficients at different levels are buffered in Toolware,
and they are dynamically packed together to form one display image as
needed. Level 0 represents the original data, or the image reconstructed
from its DWT coefficients. Obviously, the reconstructed image can be
different from the original one if certain DWT coefficients are manipulated by processing functions in the tool. The user needs to take
precaution in preserving the original image while saving new results
into the hard disk.
Wavelet decomposition
After an image is loaded into the 2-D tool, it can be decomposed by
clicking on the
button. Then, a list of wavelet decomposition sequences will pop up along with a choice of the decomposition structure.
Fig 3.6 shows an example, in which a five-level wavelet-packet structure is selected for decomposition of an image using the Daubechies
biorthogonal wavelet. The decomposition process is started and terminated automatically by clicking on the OK button.
Figure 3.6: Selection of the decomposition parameters
After Toolware has performed the decomposition based on selected
parameters, the decomposed image will pop up on the window, so that
the user can view the absolute value of the wavelet coefficients, see Fig
3.7. The displayed image is generated by using the truncated absolute
values of wavelet coefficients, but raw DWT coefficients are retained in
an internal buffer for actual processing. As needed, any other decomposition level within the decomposition range can be seen by clicking
on one of the icons
When the level i is clicked on, all
DWT coefficients from level 1 to level i are displayed in the same window. In a composed image of DWT coefficients, the upper left block is
the LL (low-low) band, the lower left block is the LH (low-high) band,
the upper right block is the HL (high-low) band, and the lower right
block is the HH (high-high) band. Although the image is decomposed
for four levels, the example in Fig 3.7 shows that we can still retrieve
level-2 wavelet packet coefficients for processing and display, since all
coefficients at every level are saved into an internal buffer. The screen
remains unchanged if an invalid level of an image is chosen. This can
happen, for example, if the user tries to view the second-level DWT
coefficients of an image not yet decomposed.
Figure 3.7: DWT coefficient display
Display thresholding
Wavelet coefficients are usually very sparse. For much better visual
effects, we display wavelet coefficients, except for the coefficients in the
lowest subband, in reverse brightness. That is, except for DWT coefficients in the lowest subband, i.e., LL, LLLL, LLLLLL, etc., larger
3.3. 2-D DWT TOOL
DWT coefficients are represented by darker display pixels. For convenience of operation, a set of buttons,
is available for quick preview of thresholding effects without changing
their internal values. The two leftmost buttons decrease/increase the
smallest (absolute) values to be displayed. The next two buttons decrease/increase the largest (absolute) values to be displayed. Next to
them, the ruler-like icon indicates the selected lower/upper bounds with
respect to the range of true values. Wavelet coefficients that fall into
the lower/upper bound setting are scaled to the display range of 0 to
255. The far right button resets all the threshold settings and returns
the image display back to its original form. We remark here that, unlike the processing functions, the display thresholding does not have
permanent effects on the internal values of the displayed DWT coefficients. The user should use built-in or custom processing functions to
alter DWT coefficients for actual signal processing purposes. Fig 3.8
shows an example in which some DWT coefficients are thresholded in
the display buffer, so that they become less visible on the screen.
Figure 3.8: DWT coefficient display after thresholding
Wavelet reconstruction
We note that once a data set is decomposed, its selected decomposition
parameters are locked into the tool to prevent erroneous, meaningless
reconstruction. When clicking on the
button, a window for selection of reconstruction parameters will pop up. Toolware can perform
reconstruction between different levels, and their results will be stored
in its internal buffers with the possibility of overwriting their existing contents. For example, if an image is decomposed into four levels,
its DWT coefficients at levels 1, 2, 3, and 4 are kept in the internal
buffers. If a level-3 to level-0 reconstruction request is selected, as in
Fig 3.9, DWT coefficients stored in level 3 buffers are retrieved and
then reconstructed back to level-2 subbands. The new level-2 values
will be saved into the level-2 buffers. Then, level-2 coefficients will be
reconstructed into level-1 coefficients, and new level-1 coefficients will
be reconstructed back to level-0. If any of level-3 coefficients have been
altered before the reconstruction request, it is likely that old level-2,
level-1, and level-0 coefficients will be overwritten in the reconstruction
Figure 3.9: Selection of DWT reconstruction
As usual, the user can view the new DWT values after reconstruction. If the user exits Toolware at this point, the original file on the
hard disk is not affected, but if the file-save button is invoked, then the
latest level-0 image will replace the original image if the same file name
is chosen.
Coefficient processing
In most signal processing applications, DWT coefficients are altered,
deleted, or clustered to achieve certain effects. The 2-D tool allows
the user to add new functions for processing of DWT coefficients in
its buffers. Processing of DWT coefficients is triggered by the
button, and a list of processing functions will pop up.
3.3. 2-D DWT TOOL
Figure 3.10: Selection of DWT processing functions
In the example shown in Fig 3.10, the user has chosen to apply a simple
thresholding routine to eight subbands of the decomposed art.pgm image. When the OK button is clicked on, this routine will be applied to
each of the subbands, one after another, until all the selected subbands
are processed. Post-processed DWT coefficients will be displayed on
the screen automatically, see Fig 3.11.
Figure 3.11: Post-processed DWT coefficients
In addition to its own internal routines, the 2-D tool will accept
user-defined processing functions, provided that they conform to the
following "dll" interface structure. More detail on the integration of
processing functions to the Toolware will also be discussed in the section
on the 1-D tool.
tfdefine PROC2D /*Tell the Toolware that this
is a 2-D processing routine*/
#define NAME "Simple Smoothing" /*The name of
this routine*/
#include "extfuncl.h" /*Some Toolware symbols*/
/* This routine accepts floating point numbers
placed in the 2-D array "input",*/
/* process the array, and then place the results
at the 2-D array "output". */
/* The dimension of the matrix is specified by
"x" and "y".*/
TOOLBOX_FUNCTION process(float huge **input,
float huge **output, int x, int y)
int i,j;
output [i] [ ] =
(input [i-1] [j-1] +input[i] [j-1]
+input[i+l] [j-l]) +
(input [i-1] [j] +input[i][j]
+input[i+1] [j]) +
(input [i-1] [j+1] +input[i] [j+1]
+input [i+1] [j+1
One needs to use a compiler to compile a new routine into a Windows
"dll" (dynamic link library) file and place it into the Toolware directory.
Otherwise, the 1-D tool will not be able to find your custom routine
for execution. In using Borland C++ 5.0, one loads the .ide project (in
C++) into the compiler and makes necessary functional changes. Then,
simply build the dll libary to be saved into the Toolware directory. If
you use other compilers to generate codes, in order to be compatible,
the .dll used must have names exported explicitly (see dll.map). If
these files do not match, Toolware will assume that the .dll is not an
external tool designed for it and will ignore it.
3.4. 1-D DWT TOOL
Figure 3.12: 1-D data file selection
1-D DWT Tool
After the 1-D DWT tool is activated, it does not display any signal
before any data file is opened. The 1-D DWT tool has the same set of
control buttons as the 2-D DWT tool, but their display and processing
mechanisms are quite different. The list of files that can be read into
the 1-D DWT tool by clicking on the
button is displayed on
the screen, see Fig 3.12.
Data file format
The 1-D DWT tool accepts numerical (integer or floating-point) files in
the ASCII format. Appending "pts" to a file name, i.e., /z/e_nome.pts,
makes it visible to the 1-D DWT tool, and the file name will appear on
the file list of Toolware. The folowing are three examples that have an
acceptable data format for the Toolware.
Style 1:
Style 2:
0.000000 0.000111 0.000444 0.000998 0.001773
0.002765 0.003973 0.005395 0.007027 0.008866
0.010906 0.013145 0.015577
Style 3:
37 40 204 80 88 163 186 131 112 157 129 120 82 69
116 74 43 73 114 73 90 123 147 149 165 193 185 168
180 203 172 165 181 182 183 177 191 210 197 219 224
148 96 255 100 1 144 88 20 7 55 45 27 74 43 58 193
74 93 179 185 113 114 172 114 10 0 87 70 100 64 38
75 109 63 85 139 135 138 161 172 188 186 173 185 206
170 163 1 93 182 171 178 196 208 194 214 223 191 74
226 181 0 123 109 33 5 42 56 26 49
After a data file is loaded into the Toolware, it is graphically displayed
on the screen in a three-row format, see Fig 3.13, and the data is
ready for DWT decomposition, processing, and reconstruction. The
three rows have three different buffers, denoted as DB1,DB2, and DB3
which will be also used for processing and reconstruction of DWT coefficients. In this example, both DB1 and DB2 contain an unprocessed
signal, and DB3 is empty. Details on management of the three display
buffers will be explained later.
1-D DWT decomposition
The 1-D tool supports both pyramidal decomposition and waveletpacket decomposition. Similar to the decomposition procedure of the
2D DWT Tool, you need to first specify a decomposition level and a
wavelet type for decomposition of a one-dimensional signal. The 1D DWT decomposition is activated by clicking on
A list of
wavelets will pop up on the screen together with decomposition structures, see Fig 3.14. At the user's choice, one needs to select the wavelet
type, the decomposition level, and the decomposition structure, i.e.,
pyramidal or wavelet packet, and then click on the OK button to start
decomposition. In this example, we have chosen a level-0 to level-4,
3.4. 1-D DWT TOOL
Figure 3.13: The three-row display of an acoustic signal
wavelet-packet decomposition of the signal by the cubic-spline wavelet.
Viewing and processing the data
The 1-D DWT tool can decompose a signal up to four levels. As in the
2-D tool, it also uses different buffers to store DWT coefficients. For
example, if a signal is decomposed for three levels, its DWT coefficients
in any of the three levels can be retrieved for processing, reconstruction, and display. When a particular subband at a level is chosen by
clicking on one of the following "level" buttons, the DWT coeffcients
will be loaded into the three display buffers DB1,DB2, and DB3 for
Figure 3.14: Selection of decomposition parameters
visualization based on the following rules.
When the user clicks on OA, the original signal is displayed on
and the reconstructed signal, if any, is displayed on DB3. The (reconstruction) difference between DB1 and DB3 is shown at DB2. If the
system does not have any reconstructed data yet, then DB3 (and thus
the third display row) is empty, and both DB1 and DB2 display the
original data because DB2 keeps the difference between DB1 and DB3.
For other decomposition levels, DB3 displays the parent subband of
the two chosen subbands displayed in DB1 and DB2. That is, at level
1A, which represents the two subbands decomposed from the original
signal at 0A, its low-pass and bandpass subbands are displayed in
original signal, is displayed on DB3. For following levels, letters A and
B respectively denote the low-pass and bandpass children of a set of
When level 2A is chosen, the low-pass and bandpass children of the
level 1A low-pass subband, which is displayed on DB3, are respectively
displayed on DB\ and DB2. Similarly, at level 2B, the low-pass and
bandpass children of the level 1A bandpass subband, which is displayed
on -D-B3, are respectively displayed in DB1 and DB2. Rules for use of
and DB2 at other levels are listed as follows.
DBi and DB2 display
children from the low-pass band of 2A
children from the bandpass band of 2A
children from the low- pass band of 2B
children from the bandpass band of 2B
children from the low-pass band of 3A
children from the bandpass band of 3A
children from the low-pass band of3B
children from the bandpass band of 3B
children from the low-pass band of3C
children from the bandpass band of3C
children from the low- pass band of 3D
children from the bandpass band of 3D
Fig 3.15 shows DWT coefficients of an signal. In its normal display
mode, the maximum and minimum values of all DWT coefficients, in-
3.4. 1-D DWT TOOL
eluding the original data, are used to set the display scales. This usually
leads to loss of visual details of small coefficients in some subbands, as
expected, for the sake of display consistency. To remedy this problem,
the 1-D tool is equipped with a sliding magnifier through which one can
better view details of small coefficient values. By simply pressing on
the left mouse button, a magnified version of data surrounding the cursor will display on a small floating window, see Fig 3.16. The zoom-in
window disappears at the release of the button.
Figure 3.15: The three-row display of DWT coefficients at level-3
For a given display level, the user can process data stored in the
to DB5 by clicking on the "P" (processing) button,
and then
the list of processing functions will pop up, see Fig 3.17. By clicking
on a processing function together with one or more buffer buttons (uppermost signal (DB1), middle signal (DB2), bottom signal (DB3)), the
user can process data stored in selected buffers by the chosen function.
Both the internal and display buffers are refreshed after the processing
function is executed.
At any time, the user can reconstruct all or some DWT coefficients.
Similar to the 2D Tool, once a signal is decomposed, its decomposition
structure is registered into Toolware, and the user can only use the selected decomposition wavelet and structure for reconstruction. In the
example shown in Fig 3.18, only the reconstruction levels can be chosen
by the user, and all other parameters will be set by Toolware. Once
the reconstruction command is executed, new level-3 DWT coefficients
are reconstructed from level-4 DWT coefficients and saved into level-3
Figure 3.16: A magnified view of small DWT coefficients
Figure 3.17: The window for 1-D DWT processing functions
3.4. 1-D DWT TOOL
Figure 3.18: Selection of the reconstruction structure
buffers. Level-2 buffers are updated in a similar fashion. This process
repeats until all levels between the chosen reconstruction levels are updated. As usual, you can view the modified signal after reconstruction.
Custom processing routines
Like the 2-D tool, the 1-D tool also accepts custom routines for processing wavelet coefficients. The following is an example routine for
smoothing of the chosen signal.
#define PROC1D
/*Tell the Toolware this
is a 1-D routine*/
tfdefine NAME "Simple Smoothing"
/*Name of the routine*/
#include "extfuncl.h" /*Toolware symbols*/
/* This routine accepts floating point numbers placed in
the array "input",*/
/* process the array, and then place the results at the
array "output". */
/* The total number of data samples is defined by "len".*/
TOOLBOX.FUNCTION process(float huge *input,
float huge *output, int len)
int i;
output[i]= (input[i-1] +input[i] +input[i+1])/3.0;
Continuous Wavelet Transform Tool
The continous wavelet transform tool (CWT tool) generates the continuous wavelet coefficients of a one-dimensional signal. It has eight
different control buttons, see Fig 3.19.
Figure 3.19: CWT tool control buttons
Starting from left to right, the first button is for selection of a data
file for processing, the second and third are for saving the displayed
CWT coefficients into an ASCII data file, or a pgm (image) file, respectively. The image of the CWT coefficient matrix can be printed to your
printer by clicking on the printer button. The relative display intensity
of CWT coefficients can be adjusted by the next four buttons. Clicking
on the fifth button, which has a straight line, means that the display
intensity directly reflects the magnitude of CWT coefficients. The next
two buttons are for (de)emphasis of small and large coefficients. The
eighth button allows you to display either only the magnitude, which is
the default mode, or signed amplitudes, with different colors assigned
to positive and negative values.
The CWT coefficients of the signal, which are organized into a twodimensional matrix, are graphically shown on the display window of
the CWT tool. The x-axis of the display is the time line, and the yaxis is the scale of the signal. After you invoke the CWT tool from
the main menu, it will display an empty window. By clicking on the
file selection button, you can select a ".pts" or ".wav" file as the signal
input. Then, the CWT tool will produce the CWT coefficients through
a self-explanatory sequence. After the signal is processed, its CWT
coefficients will be displayed on the window.
In this example displayed in Fig 3.20, the data set (file: delta.pts)
represents two compounded sinusoidal signals, in addition to two impulses. The frequencies of the two sinusoidal signals are 500 and 1000
Hz, respectively. The magnitudes of the two impulses are located at
t = 0.192 and 0.196, respectively, and their magnitude is 6. The sampling rate for this signal is set at 8 kHz. You can see from the "image"
that the CWT coefficients of the two sinusoidal signals are clearly displayed horizontally, and those of the two impulses are displayed vertically at their time instants.
Figure 3.20: CWT coefficients
Short Time Fourier Transform Tool
Similar to the CWT tool, the STFT tool generates the short time
Fourier transform coefficients of a one-dimensional signal, for a given
window size. Except for the selection of the window size, which determines the size of a signal segment for FFT computations, the STFT
tool has an identical set of control buttons, except for selection of the
short time window, and they serve the same purposes as those in the
CWT tool. By clicking on the "file" button, you can choose a onedimensional data file, in either the pts format, or the standard wav
format, to be processed by the STFT tool. Similar to the CWT tool,
the STFT coefficients of the signal are organized into a two-dimensional
matrix and then graphically displayed, where the a:-axis of the display
is the time line, and the ?/-axis is the frequency axis of the signal.
It is interesting to compare the results obtained from the CWT to
those from the STFT. The following two figures represent the results
when the window sizes are set to be w = 8 (Fig 3.21), and w = 64
(Fig 3.22), respectively. As expected, for a given window size, the
STFT has either good time resolution or good frequency resolution,
but not both. Using the CWT, we can easily determine that the signal
delta.pts has two dominating frequencies, and two impulses, from the
CWT coefficients. On the other hand, one must use different window
sizes to test this signal in order to reach the same or a similar conclusion.
Figure 3.21: STFT coefficients for window size=8
Wavelet Builder Tool
The wavelet builder tool constructs two different types of wavelets and
their scaling functions. In addition to their graphical display, the numerical values of those functions can also be exported to data files.
The B-spline functions are generated using their closed-form expressions. By choosing the B-spline family from the file selection button,
you will see the Harr wavelet at iteration 1. Each time you click on the
yellow, rightward arrow, the wavelet order increases by 1. That means
that you will see the linear, cubic, and other splines and wavelets by
clicking on the same button. Fig 3.23 displays the cubic spline and
its wavelet. The Daubechies orthonormal wavelets are generated in a
similar manner. Some precomputed coefficients are already saved in
the system for fast retrieval and export. The values of these wavelets
functions can be saved into text files for further processing. For example, you could save the numerical values of wavelet functions into data
files, and then use the STFT tool to examine their spectra.
Figure 3.22: STFT coefficients for window size=64
Figure 3.23: Cubic spline and its wavelet
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[1] Haar, A., Zur Theorie der Orthogonalen Funktionensysteme, Math
AnnaL, 69 (1910), 331-371.
[2] Chui, C. K., Wavelet: A Mathematical Tool for Signal Analysis,
SIAM Publ., 1997.
[3] Chui, C. K. An Introduction to Wavelets, Academic Press, 1992.
[4] Vetterli, M., and Kopvacevic, Wavelets and Subband Coding, Prentice Hall Inc., 1995.
[5] Strang, G., and T. Ngyuen, Wavelets and Filter Banks, WellesleyCambridge Press, 1996.
[6] Daubechies, I., Ten Lectures on Wavelets, SIAM Publ., (1992),
[7] Coifman, R. R. and M. V. Vickerhauser, Entropy-Based Algorithms
for Best Bases Selection, IEEE Trans, on Information Theory, 38
(1992), 713-718.
[8] Donohoe, D. and Johnstone, Minimax estimation via wavelet
shrinkage, Technical Report, Department of Statistics, Stanford
University, (1992).
basis function, 9, 13
basis functions, 6, 15, 19
Battle-Lamarie wavelets, 20
Battle-Lemarie, 20
Best-bases, 24
best-level, 24
biorthogonal basis, 19, 22
biorthogonal wavelet, 22
biorthogonality, 20
boundary effect, 31
broadband signal, 10
pk}, 13
qk}, 13
, 13
2:-transform, 34
2-D signal, 27
2-D wavelet decomposition, 27
2-D wavelet packet, 27
2-D wavelets, 25, 27
3db points, 3
all-pass filters, 15
analog processing, 18
antisymmetric, 22
approximate function, 10
approximation space, 19, 24, 38
approximation subspace, 10, 19
autocorrection sequence, 18
autocorrelation matrix, 18
autocorrelation sequence, 37
cardinal B-spline function, 20
chirp signal, 5
coefficient sequence, 18
Cohen, 22
compactly supported, 18, 20
complex spectrum, 2
compression, 39
continuous wavelet transform, 6
convolution, 2, 16, 38
correlation, 8
cubic spline, 13
CWT, 9
B-spline, 20, 35-37
B-spline functions, 21
B-spline series, 21, 37
B-wavelet, 20
B-wavelet Nmi 20
bandpass filter, 5
basic wavelet, 6
d-c offset, 5
Daubechies, 6, 13, 22
Daubechies orthonormal wavelet,
decomposition, 17, 27
decomposition algorithm, 17
decomposition relation, 15, 16
decomposition sequences, 15, 20,
delta function, 1, 2
detection, 37
diagonal bands, 18
digital processing, 18
dilation, 11
discrete Fourier analysis, 1
discrete Fourier transform, 35
discrete wavelet transform, 10
divide and conquer, 10
downsample, 16, 27, 38
dual B-spline, 21
dual B-spline coefficients, 37
dual B-spline series, 37
dual scaling function, 20, 21
dual spline, 21
dual spline wavelet, 21
dual wavelet, 20
dual wavelet spaces, 21
dual wavelets, 21
dual-spline, 36, 37
duals, 36
energy, 6, 24
energy concentration, 4
entropy, 24
Euler-Frobenius-Laurent Polynomial, 21
exponential decay, 21
Fast Fourier Transform (FFT),
Feauvears, 22
FFT, 31
filter bank, 10, 18, 22, 35
finite impulse response , 22
finite-energy functions, 5
finitely supported, 22
FIR filters, 22
first-order B-spline, 17
Fourier analysis, 1
Fourier processing, 6
Fourier series, 1
Fourier transform, 1
frequency bands, 23
frequency domain, 2, 18
frequency octaves, 10
fundamental frequency, 1
gating, 39
global periodic function, 1
Haar, 1
Haar wavelet, 17
Haar wavelets, 20
hard thresholding, 39
hierarchacal pyramid, 27
high-high (HH), 27
high-low (HL), 27
highpass, 27
histogram, 39
infinite product, 34, 36
infinite sequences, 21
inner product, 8, 19
interpolatory, 17
large scale, 9
linear B-spline, 17
linear combination, 6
linear phase, 22
linear transform, 6
LL component, 27
localization property, 5, 6
localized finite energy basis, 2
low frequency, 9
low-high (LH), 27
low-low (LL), 27
lowpass, 27
main lobe zero-crossing, 3
Meyer, 20
Meyer wavelet, 20
Morlet wavelet, 33
mother wavelet, 6
MRA, 10, 15
multiband signal decomposition,
neural network, 37
noise reduction, 39
noise removal, 30, 40, 41
noise statistics, 40
normalized, 19
normalized orthogonal set, 19
octave bands, 23
one-variable function, 2
orthogonal, 19
orthogonal basis, 21
orthogonal complementary subspace, 11
orthogonal projection, 17, 18
orthogonal subspace, 11
orthogonal sum, 11
orthogonality, 19, 20, 22, 37
orthonormal, 19
basis, 1, 18, 20
set, 19
wavelet basis, 20
wavelets, 6
Paseval identity, 3
perfect reconstruction, 20
periodic signals, 1
processing filter, 27
processing sequences, 22
psuedo-linear phase, 22
quadratic spline, 39
quantile thresholding, 39
recognition, 37
reconstruction, 17
reconstruction algorithm, 16
reconstruction sequences, 20
reconstructive relations, 15
rectangular function, 2
rectangular window, 2
refinement, 11
regularity, 35
Reisz basis, 6
resolution, 3
RMS, 3
sample (discretized) values, 17
scale, 10
scaling, 6
scaling function, 6, 10, 16-18,
20, 34, 35
scaling function component, 24
scaling function representation,
scaling function series, 18, 24
scaling functions, 25
scaling parameter, 10
semi-orthogonal, 19, 20
Short-Time Fourier Transform,
signal compression, 30
signal distortion, 22
signal recognition, 39
similarities, 9
sinusoidal function, 1, 9
small wave, 5
soft thresholding, 39
speckle noise, 41
spectral domain, 3-5, 9, 27
spectral energy, 3
spectral resolution, 23
spectral-domain windowing, 3
spectrum, 6
spline space, 21
splitting trick, 23
STFT, 9, 31
subspace relation, 15
symmetric, 22
tensor product, 40
tensor-product, 25
thresholding, 30
time domain, 2
time-frequency information, 2
time-dependent spectrum, 5
time-domain, 9
time-domain windowing, 3
time-frequency, 6
Tool ware, 5
transient signals, 1, 5
translate, 6, 20
translate scaling function, 36
truncation, 3
Two-dimensional wavelet packets, 27
two-dimensional wavelets, 25
two-scale relation, 13, 20
two-scale relations, 15, 16, 24
two-scale sequences, 15, 22
two-variable function, 2
uncertainty principle, 3
unique transformation, 3
universal thresholding, 39
up-sampling, 17
vector space, 19
wavelet, 5, 6, 11, 16
wavelet algorithm, 24
wavelet basis, 6
wavelet coefficient, 8
wavelet component, 24, 27
wavelet decomposition, 23
wavelet packet, 24, 27
wavelet packet algorithm, 24
wavelet packet series, 25
wavelet packet subspace, 24
wavelet packet transforms, 23
wavelet packets, 23, 24
wavelet series, 18, 24
wavelet signal processing, 6, 18
wavelet space, 21, 38
wavelet subspace, 10, 11, 19
wavelets, 35
window area, 9
window function, 2, 3, 9, 30
window measures, 9
window width, 9
window widths, 9, 10
Windowed Fourier Transform, 2
windowing effect, 3
z-transform, 21
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