[Academic Press Library in Signal Processing] Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning Volume 1 || Frames in Signal Processing

10CHAPTER

Frames in Signal Processing

Lisandro Lovisolo* and Eduardo A.B. da Silva†

*Universidade do Estado do Rio de Janeiro (UERJ), Department of Electronics and Telecommunications(DETEL)/Program of Electronics Engineering (PEL), Lab of Signal Processing,

Intelligent Applications and Communications (PROSAICO)†Universidade Federal do Rio de Janeiro (UFRJ), Program of Electrical Engineering—COPPE/UFRJ,

Department of Electronics—School of Engineering

1.10.1 IntroductionFrames were introduced by Duffin and Schaeffer [1] in 1952 for the study of non-harmonic Fourierseries [2,3]. The central idea was to represent a signal by its projections on a sequence of elements{e jλn t }n, n,∈ Z, not restricting the λn to be multiples n� of a fundamental frequency� as in harmonic(traditional) Fourier series. One can readily see that the set {e jλn t }n is highly overcomplete, in the sensethat, for example, in a space L2(− T , T ), it may consist of a sequence of elements or functions that aregreater in number than a basis for L2(− T , T ), being the last given for example by harmonic Fourierseries, among others.

As a side effect, overcompleteness may make it hard to find a representation of a function f (t) ∈L2(− T , T ) using the set {e jλn t }n , in the form

f (t) =∑

n

cne jλn t . (10.1)

This is because, due to overcompleteness, the weights or coefficients cn in Eq. (10.1) can not becomputed by the simple approach of projecting f (t) into each element of {e jλn t }n . This would beequivalent to using cn = 〈 f (t), e jλn t 〉, where 〈 f (t), g(t)〉represents the inner product

∫f (t)g∗(t)dt

and g∗(t) denotes the complex conjugate of g(t). One should note that, when using an orthonormal Basisfor representing a signal (as is the sequence of elements defining harmonic Fourier series), computingthe inner product is the standard approach for finding the cn .

Despite that difficulty, overcompleteness may bring some advantages as one may obtain more com-pact, robust or stable signal representations than using Bases [4–7]. This is why frames are beingwidely researched and employed in the last two decades in mathematics, statistics, computer scienceand engineering [4].

The basic idea leading to the definition of frames is the representation of signals from a given spaceusing more “points” or coefficients than the minimum necessary—this is the whole concept behindovercompleteness. This idea is behind modern analog-to-digital converters (ADC) that sample signalsat rates that are higher than the Nyquist rate with low resolution [8]. Since, the signal is sampled atAcademic Press Library in Signal Processing. http://dx.doi.org/10.1016/B978-0-12-396502-8.00010-3© 2014 Elsevier Ltd. All rights reserved.

561

http://dx.doi.org/10.1016/B978-0-12-396502-8.00010-3

562 CHAPTER 10 Frames in Signal Processing

high rate, the requirement on the sample precision can be relaxed [6]. This is a different perspectiveas compared to sampling based on the Nyquist criterion, when in general one assumes that a goodquantization precision is employed so that quantization errors can be neglected.

In addition to the above, there is the motivation of achieving nonuniform and/or irregular sampling(since sampling clock jitter is equivalent to irregular sampling). This has led to a large set of resultsand some applications of the “frame theory” [9]. These ideas were further extrapolated leading to thedefinition of “Rate of Innovation” of signals [10]. In this case one considers the number of degrees offreedom per unit of time of a class of signals; this is employed in [10] for sampling purposes. In someway, these concepts have lead to the so-called “Compressed Sensing” (CS) perspective [11].

Although frames provide the mathematical justification for several signal processing methods andare at the heart of the flow of ideas and achievements shortly and briefly described above, it took a longtime until frames came definitely into play. The first book concerning the topic was written by Youngin 1980 [2]. In [12] Daubechies, Grossman and Meyer realized that frames can somehow be understoodin a way very similar to expansions using bases. This established the link among frames and wavelets[5,13,14], and new breath was given to the research on frames. Applications of the “frame theory” havebroadly appeared. Frames have been an active area of research, allowing the production of a plethoraof theoretical results and applications. Good material on frames can be found in [4,5,13], and a bookdedicated to this topic is [6]. We try to show a few of the relevant results regarding frames in this chapter.

1.10.1.1 NotationIn the following we use x, to denote a signal in a discrete space, for a finite dimensional discrete spacethe same notation is employed. The value of the nth coordinate or sample of x is referred to by x[n].A bold capital is used for matrices, as G. Continuous functions or signals are explicitly denoted withreference to the independent variable, as in x(t). When just a letter like x is employed it is becausethe definition or result applies in both cases—discrete or continuous spaces. ‖x‖ refers to 〈x, x〉. Theinner product 〈f, g〉 is to be understood as 〈 f (t), g(t)〉 = ∫

f (t)g∗(t)dt in continuous spaces, and〈f, g〉 = ∑

n f[n]g∗[n] in discrete spaces.

1.10.2 Basic conceptsLet us try to briefly introduce what are frames. A frame of a space H is a set of elements that spans H.Therefore, a frame G = {gk}k∈K (a set of elements gk which are indexed by k ∈ K), can be used toexpress any x ∈ H by means of

x =∑k∈K

ckgk, (10.2)

where the ck are called the frame coefficients.The only restriction that we have imposed on G = {gk}k∈K for it being a frame is that it should span

H. Therefore, G is in general overcomplete, meaning that the set of frame coefficients {ck}k∈K that canexpress x by means of Eq. (10.2) is not unique, as the overcompleteness of G means that its elementsare not linearly independent.

From above one sees that there may exist different ways to compute the frame coefficients ck used inEq. (10.2) to synthesize x. We employ interchangeably both terms “expansion” as well as representation

1.10.2 Basic Concepts 563

since all the frame elements (generally in larger number than the space dimension) may a priori beemployed to express the signal. Due to overcompleteness one usually employs more elements than thesupport or dimension of the signal space H to represent the signal. This is equivalent to saying thatthe frame coefficients for a given signal may be in larger number than the coefficients employed in thecanonical signal representation.

Example 10.1. Consider the set of elements G in R2,G = {[0 1/2], [1/4 −1/4], [−1/2 0]}. Any

vector x = [x[0], x[1]] projected into these elements gives a set of coefficients:

c =⎡⎣ c0

c1c2

⎤⎦ =⎡⎣ 0 1/2

1/4 −1/4−1/2 0

⎤⎦[x[0]x[1]

]=⎡⎣ x[1]/2(

x[0] − x[1]) /4−x[0]/2

⎤⎦ . (10.3)

The original vector can be obtained from c using, for example

[x[0]x[1]

]= GTc =

[0 0 −22 0 0

]⎡⎣ c0c1c2

⎤⎦ or

[x[0]x[1]

]= GTc =

[0 0 −20 −4 −2

]⎡⎣ c0c1c2

⎤⎦ ,(10.4)

among several other possibilities for GT. That is, the GT that reconstructs x from its projection on a setG is not unique.

Consider that G is the matrix constructed from the frame elements by stacking them as rows. G isused to compute the coefficients vector c using

c = Gx. (10.5)

Note that in the current example dim(x) = 2×1, dim(G) = 3×2, dim(c) = 3×1 and dim(GT) = 2×3.If a frame for R

N has K > N elements, one has that dim(x) = N × 1, dim(G) = K × N , dim(c) =K × 1 and dim(GT) = N × K . That is, one has that the matrix GT used to obtain x = GTc must besuch that GTG = IN (IN is the identity matrix of dimension equating the dimension of the vectors inG, which is 2 in the present example).

Obviously, the roles of the matrices G and G above can be interchanged. Since GTG = IN one hasthat GTG = IN , and thus one can employ G as a projection operator on x for finding a set of coefficientsc, and reconstruct x using

x = Gc. (10.6)

Note that in this case one can make

GT =[

1 2 −11 −2 −1

], (10.7)

or define G as in (10.4). �As we have seen in the example above, one should note that the set c is redundant in the sense that

the vectors employed to compute the {ck}k∈K (onto which x is projected) may be linearly dependent.One advantage of this is that the wrong computation of some coefficients of the expansion or eventheir losses may still allow the recovery of the original signal from the remaining coefficients within


an acceptable error, as one has different possible signal reconstruction operators provided by the frameconcept. These ideas have led to applications of frame expansions for signal sampling with low quanti-zation requirements [4], and also to the development of transmission systems that are robust to erasures(when data is lost or erasured) over a network, which corresponds to the deletion of the correspondentframe elements [15–17].

1.10.2.1 The dual frameThe above example links to the strategy for computing frame coefficients using the inverse or dual frame[4–7], that provides the dual reconstruction formulas

x =∑k∈K

〈x, gk〉gk =∑k∈K

〈x, gk〉gk . (10.8)

From Eq. (10.8) one may define G = {gk}k∈K as the inverse or dual frame to the frame G = {gk}k∈K.For a given signal x, since G is overcomplete, G is, in general, not unique [6], as shown in Example10.1.

Considering a frame expansion of a signal—the set of coefficients ck , the frame expansion can beviewed as a measure of “how much” the signal “has” of each frame element {gk}k∈K. As it can be readilyseen, this property can be used to infer or analyze signal characteristics. Since in an overcomplete frameits elements are linearly dependent, at least two of its elements are not orthogonal. This implies that char-acteristics that are different, but similar to one another, can be effectively observed by projecting the sig-nal into different frame elements that are similar to these characteristics. If an orthogonal basis was usedinstead, such characteristics would not be as evident from the projections, since each projection wouldhave at most a small amount of each characteristic. This way, the similar characteristic would be mixedamong different projections, and would therefore not be as evident as in the case of a redundant frame.

The reasoning in the above paragraph has been largely employed to justify the use of the so-calledGabor frames, that are discussed in Section 1.10.5.4. Gabor frames are constructed by modulation andtranslation of a prototype function, providing thus a time-frequency analysis capability [4,18–22]. Gaborframes employ sets of time and frequency translated versions of a predefined function to analyze orsynthesize a signal. These frames are named after Gabor due the their origins in [23]. The main idea is toexplore functions that have some desired energy concentration in time and frequency domain to analyzethe content or information that is represented in a signal. Several tools have been developed for achievingthis, using the decomposition of a signal into a Gabor frame [24–26]. As the example regarding theGaborgram in Section 1.10.6.2 ahead shows, the inverse frame elements may not resemble the frameelements and some care must be taken when using frame expansions to capture specific signal features.

Although the elements of a frame that are used to express a signal are in general selected a priori,frame expansions allow for the use of elements with special properties. The Balian-Low theorem [13]shows that it is not possible to construct a basis with elements that are well localized in both timeand frequency domains simultaneously. Frames do not impose uniqueness to the signal representation,therefore the definition of the frame elements is less restrictive than it is for basis elements. Hence, we canachieve, for a frame, a better localization of its elements simultaneously in time and frequency domainthan for a basis, better dealing with the limitations imposed by the uncertainty principle [4,19,21].

1.10.2 Basic Concepts 565

The property of frames discussed above has been largely employed by the so-called WindowedFourier Analysis schemes [4,27]. In addition, since the frame coefficients may not be unique for a givensignal, the set of frame coefficients can be altered or improved—considering the linear dependenciesamong frame elements—in order to highlight desired signal features. Moreover, frames can be designedfor specific applications depending on the features that one desires to extract or analyze in the signal.Examples of such specific designs are ones using wavelets [4,5,7] and other time-frequency [4,28] orstructurally/ geometrically oriented analysis techniques [29,30].

In general, the computational burden of obtaining signal representations into frames surpasses thecomputational demands of traditional expansions into basis, as, for example, the ones of the Fast FourierTransform (FFT) [31]. However, as mentioned above, with frame expansions we can obtain a separationof signal structures that is much higher than the one that is possible using traditional approaches. Mallat[4] explores this in his proposed super-resolution techniques.

Due to the characteristics above, frame applications range from signal coding [4], signal sampling[1,4,6], signal analysis [4,5] and transient detection [24,25], to communication systems design [6,32].Frames have also been related to the analysis and design of Filter Banks [33,34].

1.10.2.2 Signal analysis and synthesis using framesA frame or its dual can be employed to analyze a signal and obtain the signal expansion, i.e., the framecoefficients, and to synthesize it from the coefficients obtained in the analysis process. This is shown inFigure 10.1. As it can be noticed, from Eq. (10.22), the roles of {gk}k∈K and {gk}k∈K can be interchangedin Figure 10.1, providing different perspectives for signal analysis.

gK

g1

g2

gK

g1

g2

x x = k ckgk

c1 = x↪ g1

c2 = x↪ g2

ck = x↪ gk↪

Analysis Synthesis

FIGURE 10.1

Analysis and synthesis of a signal using a frame and its inverse—as noted in the text, the roles can beinterchanged.


One notes that the “system model” of Figure 10.1 is very similar to the one employed in severalsignal processing tasks—for example in filter banks. This resemblance between frames and severalsignal processing tasks provides a large range of potential applications for frames. To highlight animportant feature, the frame elements may be designed to highlight or analyze desired signal featuresthat may be “overlapping” or linearly dependent.

In the scenario of filter banks, a relevant and special case is the one of a “perfect reconstruction filterbank” [31]. In this case the analysis and reconstruction filter banks correspond to a pair of dual frames.A more detailed analysis of the input-output mapping in this case is provided in Section 1.10.4.1 wherewe discuss frames of discrete spaces.

Some examples of common frame constructions for signal analysis purposes are Gabor and Waveletframes. These provide expansion systems or representations that have different supports on the time-frequency plane. We will discuss them in Sections 1.10.5.4 and 1.10.5.5 respectively.

1.10.3 Relevant definitionsNow, that we have discussed some basic properties of frames, we are ready to present a more formaldefinition.

Definition 10.1. A sequence of elements G = {gk}k∈K in a space H is a frame for H if there existconstants A and B, 0 < A < B < ∞, such that [4–7]

A‖x‖2 ≤∑k∈K

|〈x, gk〉|2 ≤ B‖x‖2, ∀x ∈ H. (10.9)

i. The numbers A and B are called the lower and upper frame bounds, respectively, and are not unique.The optimal lower frame bound is the supremum on all A and the optimal upper frame bound isthe infimum on all B [6].

ii. It is said that the frame is normalized [5] if ||gk || = 1, ∀k ∈ K. �A commonly addressed problem, related to frames, is how to determine if a given sequence of

elements {gk}k∈K in H is a frame of H. This is often refereed to as the frame characterization problem[6]. This characterization is commonly accomplished by the frame bounds. From Eq. (10.9), one notesthat if for any signal one obtains A = 0 then the signal is orthogonal to all the elements {gk}k∈K and thesignal can not be represented using these elements. That is the reason for the requirement that A > 0.Also, if for a given signal there is no upper bound in Eq. (10.9) then this means that the elements aretoo “dense” for that signal and the frame does not provide a stable representation.

Given a set of elements in a space H, in order to verify if it is or is not a frame one may compute theframe bounds. It is in general much easier to find the upper frame bound B than the lower frame bound A.

Example 10.2. For instance, note that in a space RN any quantity M > N of finite norm vectors will

always provide a B < ∞. However, for the lower bound defines if the set spans or not H and in thiscase a more careful analysis is required. �

1.10.3.1 The frame operatorLet us now define the frame operator and state the frame decomposition result.

1.10.3 Relevant Definitions 567

Definition 10.2. Define the frame operator S{·} as

S : H → H, S{x} =∑k∈K

〈x, gk〉gk . (10.10)

From this definition some properties hold [6]:

i. The frame operator is bounded, invertible, self-adjoint and positive. Therefore, it assumes an inverseS−1{·}. This is referred to as the inverse frame operator.

x = S{S−1{x}} =∑k∈K

〈S−1 {x} , gk〉gk . (10.11)

ii. If the lower and upper frame bound of {gk}k∈K are, respectively A and B, then{

S−1{gk}}

k∈K is aframe with bounds B−1 and A−1, and the frame operator for

{S−1{gk}

}k∈K is S−1. �

From the definition of the frame operator one has the frame decomposition result that provides thereconstruction formulas [6]

x =∑k∈K

〈x, gk〉S−1{gk} =∑k∈K

〈x, S−1{gk}〉gk (10.12)

Note that this provides a way to compute the gk = S−1{gk} which are the elements of the inverse frameor the so-called canonical dual.

Example 10.3. Suppose that a normalized frame{gk

}k∈K is used to express the information of a

signal x, to transmit or store this information and to reconstruct x. In this scenario, assume that onetransmits/stores the frame coefficients

ck = 〈x, S−1 {gk

}〉, (10.13)

where we employ the inverse frame operator. From the definitions one has that

x =∑

k

ckgk . (10.14)

However, due to quantization coefficients are corrupted by noise. In this case one must reconstruct x

x =∑k∈K

(ck + wk

)gk =

∑k∈K

(⟨x, S−1 {gk

}⟩+ wk

)gk . (10.15)

Obviously, due to the frame reconstruction one has that

x = x +∑k∈K

wkgk . (10.16)

The reconstructed signal is corrupted by noise.


One should note that in this scenario, we know that the ck (obtained by means of Eq. (10.13) are theexpansion of a signal over a frame). However, this may not be the case for the set of coefficients{

〈x, S−1 {gk

}〉 + wk

}k∈K (10.17)

used to reconstruct x in Eq. (10.15). Assuming that wk ∈ �2(N)—i.e., the noise is bounded, that factmay be compensated by projecting

{〈x, S−1{gk

}〉 + wk}

k∈K using the operator [4,6]:

Q {ck} ={⟨∑

k

ck S−1 {gk

}, g j

⟩}j∈K

. (10.18)

Using this operator in the projected-quantized frame coefficients scenario above one has that (applyQ{·} to Eq. (10.17)

Q{〈x, S−1 {gk

}〉 + wk

}k∈K = {ck}k∈K + Q {wk}k∈K . (10.19)

Using this, the signal can be reconstructed by means of

x =∑k∈K

(ck + Q {wk}

)gk = x +

∑k∈K

Q {w}k gk . (10.20)

Supposing that the frame{gk

}k∈K is normalized, considering the quantization noise in Eq. (10.15) to

be white and zero-mean, using E[·] to denote the expected value, then in [4] it is shown that

E[|Q {w} |2

]≤ 1

AE[|w|2

]. (10.21)

That is, the resulting noise when reconstructing the signal by means of the processed coefficients as inEq. (10.19) is reduced if A (the lower frame bound) is greater than one. �

1.10.3.2 The inverse frameDefinition 10.3. The inverse frame G = {gk}k∈K to a frame G = {gk}k∈K, is the one that providesthe reconstruction formulas

x =∑k∈K


〈x, gk〉gk . (10.22)

�Example 10.4. Let us go back to Example 10.1, where we have seen two different possibilities for“inversion” of the frame given by the rows of the matrix

G =⎡⎣ 0 1/2

1/4 −1/4−1/2 0

⎤⎦ . (10.23)

1.10.3 Relevant Definitions 569

However, one can now find its canonical dual, associated with the inverse frame operator. Using apseudo-inversion algorithm [4] one finds the pseudo-inverse G+ to G(G+G = I2):

G+ =[

1/3 2/3 −5/35/3 −2/3 −1/3

]. (10.24)

Note that, this is just another example of possible frame inversion, as it is shown in [4], since G assumesan infinite number of left inverses. �Definition 10.4. When A = B the frame is said to be tight [6] and

S−1{·} = S{·}/A. (10.25)

In addition:

i. Frames for which A ≈ B are said to be snug [34] and for these S−1{·} ≈ S{·}/A. �

Definition 10.5. A frame G = {gk}k∈K is said to be normalized when

‖gk‖ = c,∀k ∈ K, c > 0 is a real constant. (10.26)

�

1.10.3.3 Characterization of frames: basic propertyFrame bounds provide limits for the energy scaling of signals when they are represented using theprojections into the frame elements. Due to that, frames are often characterized in terms of their framebounds. It is very common to define the frame bounds ratio A/B [13]. It is also possible to define the“tightness” of a frame from its frame bounds ratio: the closer that A and B are, then the tighter the frame is.

Daubechies [5] shows that the use of the frame operator (Eq. (10.10)) gives

S{x} = A + B

2

(x − R{x}), i.e., x = 2

A + B

∑k∈K

〈x, gk〉gk + R{x}, (10.27)

where R{x}can be understood as the error incurred in reconstructing x from the projections of x intothe frame elements instead of into the inverse frame elements.

In addition, she also shows that

R{x} = I {x} − 2

A + BS{x}, (10.28)

where I {x} is the identity operator. Therefore one has that

− B − A

B + AI {x} ≤ R{x} ≤ B − A

B + AI {x} −→ ‖R{x}‖ ≤ B − A

B + A‖x‖. (10.29)

Hence, one sees that if B/A is close to one, then the error incurred in the reconstruction by equatingthe inverse frame to the direct frame is small. This error gets smaller as A and B become closer.


Therefore, from an analysis-synthesis perspective if a frame is tight there is no need to find its inverse.Tight frames are self-dual [35], that is, the inverse frame to a tight frame is a scaled version of the frameitself.

For normalized frames, Daubechies calls A+B2 the frame redundancy. Several studies have been

developed on normalized tight frames. For example [36] shows that these frames have some noisereduction property. That is, if a given reconstruction distortion is required one can use less bits torepresent the frame coefficients in comparison to the precision required to represent basis coefficients.This is related to the acquisition-quantization problem we have discussed previously.

Example 10.5. Let {e1, e2, e3} be an orthonormal basis of the three-dimensional space. Define thevectors:

g1 =√

2

2e1 +

√2

2e2 g2 =

√2

2e2 +

√2

2e3 g3 =

√2

2e1 +

√2

2e3

g4 = 1

2e1 +

√3

2e2 g5 = 1

2e2 +

√3

2e3 g6 =

√3

2e1 + 1

2e3.

For any x ∈ R3 we have that

∑k

|〈x, gk〉|2 =∣∣∣∣∣⟨

x,

√2

2e1

⟩+⟨

x,

√2

2e2

⟩∣∣∣∣∣2

+∣∣∣∣∣⟨

x,

√2

2e2

⟩+⟨

x,

√2

2e3

⟩∣∣∣∣∣2

+∣∣∣∣∣⟨

x,

√2

2e1

⟩+⟨

x,

√2

2e3

⟩∣∣∣∣∣2

+∣∣∣∣∣⟨x,

1

2e1

⟩+⟨

x,

√3

2e2

⟩∣∣∣∣∣2

+∣∣∣∣∣⟨x,

1

2e2

⟩+⟨

x,

√3

2e3

⟩∣∣∣∣∣2

+∣∣∣∣∣⟨

x,

√3

2e1

⟩+⟨x,

1

2e3

⟩∣∣∣∣∣2

= 2 |〈x, e1〉 + 〈x, e2〉 + 〈x, e3〉|2= 2‖x‖.

Therefore the six vectors define a tight frame with A = B = 2. This number can in some sense beunderstood as a measure of redundancy of the frame expansion [4]. �

Tight frames turned out to be relevant, due to their operational simplicity and applications. Forexample tight frames were shown to be the most resistant to coefficients erasures [15]. Erasures meansthat when transiting information over a network one knows that the information is unreliable. If a frameis used to compute the information that is transmitted over the network then the reconstruction errorunder erasures is lower for a tight frame than for non tight ones. Tight frames can also provide sparsesignal decompositions [37]. Due to these and other interesting properties a lot of attention has beengiven to the construction of tight frames [16,37–40].

1.10.4 Some Computational Remarks 571

1.10.4 Some computational remarksIn general signal processing algorithms are implemented in digital processors. In this case, the signalis discrete and some simplifications take place.

1.10.4.1 Frames in discrete spacesConsider the case of discrete spaces as the �2(Z) (the space of squareable summable sequences). In gen-eral, in such spaces, signal analysis is accomplished by using sliding-windows. This is actually the casefor Filter-Banks [31], using the structure depicted in Figure 10.1, the synthesis equation becomes [33,34]

x[n] =K−1∑k=0

∞∑m=−∞

ck,mgk,m[n]. (10.30)

In the above equation each signal’s sample x[n], n ∈ Z, is synthesized as a sum of the samplesgk,m[n] = gk[n − m N ], N ≤ K , K is the cardinality of the frame, the gk,m are translated versionsof the gk , referring to the mth N-length signal block.

Similar concepts can be brought to frames in �2(Z), bearing in mind the correct usage of a frameand its dual. In this case, one should note that Eq. (10.30) must be understood as

x[n] =K−1∑k=0

∞∑m=−∞

〈x, gk,m〉gk,m[n]. (10.31)

That is, as discussed in Section 1.10.2.2, the frame and its dual should be employed in the two distincttasks of analysis/decomposition and synthesis/recomposition of x.

The previous definitions on frames considered any space H. When considering �2(Z), the space inwhich digital signal processing applications exist (as well as in finite vector spaces—which are discussedin the next subsection) using finite support vectors may be handy, as in FIR Filters.

A signal x ∈ �2(Z) can be expressed using Eq. (10.30) if the family of functions [33]

G = {gk,m : gk,m[n] = gk[n − m N ], k = 0, 1, . . . , K − 1,m ∈ Z

}(10.32)

is a frame for �2(Z). This result provides an important feature of frames for �2(Z).

Definition 10.6. A set of vectors G as defined in Eq. (10.32) (block translated) is a frame of �2(Z), ifthere exist constants 0 < A < B < ∞ such that for any x ∈ �2(Z) one has

A‖x‖2 ≤K−1∑k=0

∞∑m=−∞

∣∣⟨x, gk,m

⟩∣∣2 ≤ B‖x‖2. (10.33)

�One should note that if Eq. (10.33) holds for G as in Eq. (10.32), then there exists another frame

G = {gk,m : gk,m[n] = gk[n − m N ], k = 0, 1, . . . , K − 1,m ∈ Z

}(10.34)


that can be employed for obtaining the coefficients ck,m in Eq. (10.31), Obviously, the roles of G and Gcan be interchanged, providing

x[n] =K−1∑k=0

∞∑m=−∞

〈x, gk,m〉gk,m[n]. (10.35)

As it can be noted, given G as in Eq. (10.32), the G as (10.34) satisfying Eqs. (10.31) and (10.35) isnot unique.

1.10.4.2 Finite vector spacesIn a finite vector space H

N one in general restricts the frame to have K elements; and thus Eq. (10.9)becomes

A‖x‖2 ≤K∑

k=1

|〈x, gk〉|2 ≤ B‖x‖2, x ∈ HN . (10.36)

In this case, as motivated in previous examples some computational simplifications take place.

Definition 10.7. Analysis and Synthesis operators:

i. Let the synthesis operator be

T {·} : CK → H

N , T {ck}k=Kk=1 =

K∑k=1

ckgk, (10.37)

where CK is a K-dimensional complex vector space.

ii. Let the analysis operator T ∗{·} (adjunct operator of T {·}) be given by

T ∗{·} : HN → C

K , T ∗{x} = {〈x, gk〉}k=Kk=1 . (10.38)

iii. The operator T {·} synthesizes x from the frame coefficients ck that are obtained by the analysisoperator of a dual frame given by

T ∗{·} : HN → C

K , T ∗{x} = {〈x, gk〉}k=Kk=1 . (10.39)

iv. Using the analysis and synthesis operators the frame operator is then given by

S : HN → H

N , S{x} = T {T ∗{x}} =K∑

k=1

〈x, gk〉gk . (10.40)

�

In vector spaces the operators T {·} and T ∗{·} can be interpreted as matrices [6], as in the examplesprovided before.

1.10.4 Some Computational Remarks 573

Assuming that x is a column vector that is dim(x) = N × 1 and the coefficients are organized in acolumn vector c(dim(c) = K × 1) as well. One has that

x dim(x) = N × 1

c dim(c) = K × 1

gk dim(gk) = N × 1

T =⎡⎢⎣ gT

1...

gTK

⎤⎥⎦ dim(T) = K × N (10.41)

T {c} = TTc dim(TT) = N × K (10.42)

T ∗{x} = Tx dim(T∗) = K × N (10.43)

S{x} = T {T{x}} = TTTx = Sx dim(S) = N × N (10.44)

S−1{x} =(

TTT)−1

x = S−1x dim(

S−1)

= N × N . (10.45)

The dual frame elements can be computed by means of

gk = S−1{gk} =(

TT T)−1

gk, (10.46)

and we have that

T {c} = TT c = T(

TTT)−1

c dim(TT) = N × K (10.47)

T ∗{x} = T∗x =(

TTT)−1

Tx dim(T∗) = K × N . (10.48)

Example 10.6. As we have seen above S = TT T. Let ρi be the eigenvalues of S, then the framebounds are given by A = mini ρi , and B = maxi ρi [6]. Thus, if TT T = AIN (IN is the identity matrixof size N) then the frame is tight (A = B). Hence, for a tight frame S−1S = IN . �Example 10.7. The Gram matrix or Gramian of a sequence of vectors {gk}k∈K is the matrix G,whose elements are defined by gi, j = {〈gi , g j 〉}i, j∈K. It has been employed to analyze different framescharacteristics. For example, in [41], it is employed to investigate the symmetry properties of tightframes, providing tight frames design procedures. In [37] the Gramian is also employed for analyzingand designing frames with desired features.

We have seen the analysis, synthesis, and frame operators, and its inverse frames counterparts. As onemay notice, a lot of the frame characteristics can be extracted from them. Now, define the Gram matrixG = T T T . One readily notes that dim

(G) = K × K and that each of its entries gi, j = {〈gi , g j 〉}i, j∈K.

This Gram matrix provides how similar are the frame elements one to another, therefore, it can beunderstood as an indicative of the connections among the different ck . �Example 10.8. In a vector space if the lower frame bound A were zero for a signal x then theframe elements would all be orthogonal to x, and the frame would not be capable of representing x. Inaddition, note that if the frame has a finite set of elements, then, necessarily, due to the Cauchy-Schwartzinequality, the upper frame bound condition will be always satisfied. �


1.10.5 Construction of frames from a prototype signalIt is common to process signals in order to search for specific patterns in time, frequency or even scale.In this case, one often a priori specifies a set of desired behaviors or patterns to be searched in signalsto be analyzed. These are specified as a set of pre-defined waveforms. If synthesis is not required therestrictions on the set are milder than in the case when synthesis is required. In the later case, one shouldguarantee the set of pre-defined functions or waveforms to be a frame. This is what we discuss now.We suppose a signal processing scenario where a given signal has to be analyzed and synthesized usinga set of pre-defined waveforms that are derived from operations such as change of position in time(translation), change of frequency (modulation) and change of size (dilation) over a prototype signal.In this section we discuss some conditions that make a frame to be generated when these operations areapplied to a prototype waveform.

1.10.5.1 Translation, modulation and dilation operatorsThere are several ways to construct frames from operations on a prototype signal, some of them are basedon translations, modulations and dilations of a function g(t) ∈ L2(R)(the space of square integrablefunctions) [6].

Definition 10.8. Translation by a ∈ R:

Ta : L2 (R) → L2 (

R), Tag(t) = g

(t − a

). (10.49)

�Definition 10.9. Modulation by b ∈ R:

Eb : L2 (R) → L2 (

R), Ebg(t) = g(t)e2π jbt . (10.50)

�Definition 10.10. Dilation by c ∈ R − {0}:

Dc : L2 (R) → L2 (

R), Dcg(t) = 1√

cg

(t

c

). (10.51)

�

1.10.5.2 Common frame constructionsThe most common approaches to construct frames from a prototype are:

• A frame in L2(R) constructed by translations of a given function g(t) ∈ L2(R), through

{Tnag(t)}n∈Z, with a > 0, (10.52)

is called a frame of translates [6];

1.10.5 Construction of Frames from a Prototype Signal 575

• A Weyl-Heisenberg or Gabor frame is a frame in L2(R) constructed from a fixed function g(t) ∈L2(R), by means of

{EmbTnag(t)}m,n∈Z, with a, b > 0. (10.53)

Such frame is also called a Gabor System or a Windowed Fourier Frame [4–6,42] due to its connectionto time-frequency analysis techniques;

• A frame constructed by dilations and translations of a prototype (mother) function g(t) ∈ L2(R) by

{Tnac j Dc j g(t)} j,n∈Z ={

c− j2 g(

c− j t − na)}

j,n∈Z, with c > 1, and a > 0. (10.54)

is called a wavelet frame [4–7].

In the following subsections we enumerate some conditions for obtaining frames in the above waysand some examples of their applications or relevance.

1.10.5.3 Frames of translatesDigital signal processing is heavily based on signal sampling, that is, the capability of sampling afunction (whose independent variable may not be time) using regular intervals and reconstructingthe signal from the samples [4,6,31,43]. A sample corresponds to a value that in turn represents anevaluation/measurement of the function/signal. The operation of sampling can be modelled as theprojection of the function/signal over a pre-defined element. Regular sampling corresponds to translatingthe element into different positions regularly sampled and taking a sample (measurement/projection) atthe correspondent position. Although the following results employ time as the independent variable itcould be any other.

Definition 10.11. A frame of translates is a frame for L2(R)obtained through operations {Tna g(t)}n∈Z,on a fixed function g(t) with a > 0. �

Which conditions should hold on g(t) in order to {Tnag(t)}n∈Z being a frame? In this case it can beshown that [6] {Tnag(t)}n∈Z is a frame with bounds A and B if and only if

a A ≤ G(ω) ≤ aB, ω ∈ [0, 1] − N , N = {γ ∈ [0, 1] : G(ω) = 0}. (10.55)

Frames of translates are intimately related with sampling. The example below highlights their con-nection with Shannon Sampling Theorem [43].

Example 10.9. In this example one employs the Shannon Sampling Theorem [43,44]. Let a bandlimited function of time s(t) be sampled using a train of impulses at a rate 1/T . Assume that the signalbandwidth is W. If T < 1

2W , one can reconstruct the signal using

s(t) =∑

n

s(nT )sin[t − nT ) πT

](t − nT ) πT

. (10.56)

This is so because the infinite set of sinc pulses centered at nT provides a basis/frame of the W Hzband-limited space.


Define R as the “amount of over-sampling”

R = 1

2W T, forR ≥ 1, (10.57)

One notes that the Nyquist rate [31] implies 2W T = 1 and R ≥ 1 makes 1/T ≥ 2W . One has that [44]

s(t) = 1

R

∑n

s(nT )sin[(t − nT ) πRT

](t − nT ) πRT

. (10.58)

Note that for R ≥ 1, two sinc pulses centered at kT and lT (k, l ∈ Z) not just overlap, but areactually not orthogonal. Indeed, the set of T temporally-spaced sinc pulses is an overcomplete basis ofthe W-bandlimited signal space, in the case R ≥ 1. That is, the sinc pulses centered at nT with R > 1are not linearly independent. However, the infinite set of sinc pulses above is shown to be a tight frameand R is interpreted as a redundancy factor for the frame [10,44]. One notes that for R = 1 (10.58)equates (10.56) where the signal samples occur where the sinc pulses equate one (for t = nT ) or zero(other cases). However, as R increases the sinc pulse centered at nT gets wider and is not zero anymorefor t − nT = kT , k ∈ Z

∗. �

1.10.5.4 Gabor framesWhile the introduction of the frame concept remounts to 1952 [1], according to Christensen [6], the firstmention of what are now called Gabor of Weyl-Heisenberg frames can be traced back even before thatto the work of Gabor on communications [23] in 1946 and to the book of von Neumann on quantummechanics originally published in 1932 [45].

Let us simplify the idea of Gabor and simply state that its vision was to represent a signal s(t) bymeans of

s(t) =∑

m

∑n

cm,n EmbTnag(t). (10.59)

The function g(t) is chosen to be a Gaussian due to its time-frequency concentration [19,21,46]. Thisrepresentation can provide an analysis of the time frequency content around the points (na,mb) in thetime-frequency plane. The resemblance between this idea and the frame definition sparks, and then astraightforward definition emerges.

Definition 10.12. A Gabor frame is a frame for L2(R)obtained through operations {EmbTnag(t)}m,n∈Z,on a fixed function g(t) with a, b > 0. �

Now, one has the problem of guaranteeing that the set {EmbTnag(t)}n,m∈Z is capable of representingany signal s(t) ∈ L2(R), i.e., is the set {EmbTnag(t)}n,m∈Z2 a frame of L2(R)? In addition, how doesone compute the set

{cm,n

}n,m∈Z2 that allows the synthesis of a signal using Eq. (10.59)? The answer

to the later question lies in the concept of inverse frame. But, we will start from the first question: fora given g(t), when {EmbTnag(t)}m,n∈Z is a frame? The answer depends on a complicated interplaybetween g(t), a and b. For example in [47] a set of non-intuitive conditions was shown to hold on aand b to generate a Gabor System based on the characteristic function. In what follows, several resultscollected from the frame literature are presented, which provide either sufficient or necessary conditionsfor {EmbTnag(t)}m,n∈Z to constitute a frame.


b

a

Frequency

Time

FIGURE 10.2

Gabor system elements location in the time-frequency plane.

For {EmbTnag(t)}m,n∈Z to compose a frame it is necessary that ab ≤ 1 [4–6]. That is, if ab > 1a frame will not be obtained; however, the assumption ab ≤ 1 does not guarantee the generation of aframe for any g(t), see for example [47]. It should be observed that ab is a measure of the density of theframe in the time-frequency plane [4,5,19–21,42]; the smaller ab is the denser is the frame. Figure 10.2illustrates this concept.

If {EmbTnag(t)}m,n∈Z constitutes a frame then the frame bounds necessarily satisfy [6]

∀t ∈ R, A ≤ 1

b

∑n

|g(t − na)|2 ≤ B (10.60)

∀ω ∈ R, A ≤ 1

b

∑k

∣∣∣∣g (ω − kb

2π

)∣∣∣∣2 ≤ B. (10.61)

A well known sufficient condition for {EmbTnag(t)}m,n∈Z to be a frame is presented in [14]. Let a, b >0, g(t) ∈ L2(R), denote g∗(t) the complex conjugate of g(t) and suppose that ∃A, B > 0 such that

A ≤∑n∈Z

|g(t − na)|2 ≤ B∀t ∈ R and (10.62)

∑k �=0

∣∣∣∣∣∣∣∣∣∣∑n∈Z

Tnag(t)Tna+ kb

g∗(t)∣∣∣∣∣∣∣∣∣∣∞< A, (10.63)

then {EmbTnag(t)}m,n∈Z is a Gabor frame for L2(R).


A more general sufficient condition for the generation of a frame {EmbTnag(t)}m,n∈Z for a, b > 0given and g(t) ∈ L2(R) is [6,48]: if

B :=1

bsup

t∈[0,a]

∑k∈Z

∣∣∣∣∣∑n∈Z

g(t − na

)g∗(

t − na − k

b

)∣∣∣∣∣ < ∞, and (10.64)

A :=1

binf

t∈[0,a]

⎡⎣∑n∈Z

∣∣g (t − na)∣∣2 −

∑k �=0

∣∣∣∣∣∑n∈Z

g(t − na

)g∗(

t − na − k

b

)∣∣∣∣∣⎤⎦ > 0 (10.65)

then {EmbTnag(t)}m,n∈Z is a frame for L2(R) with frame bounds A, B. Note that this result showsthat if g(t) has a limited support [0, 1/x] then for any set ab ≤ 1 and b < x a Gabor frame isobtained.

Other conditions for the generation of Gabor frames exist (see for example [4–6,27]). In [49] anextension of the results in Eqs. (10.64) and (10.65) for irregularly sampled time-frequency parameters(when the set (an, bm) is replaced by any pair

(an,m, bn,m

) ∈ [na, (n + 1)a] × [mb, (m + 1b]) isprovided.

In subSection 1.10.6.2, we discuss the use of such frames for time-frequency analysis by means ofthe Gaborgram and in Section 1.10.6.3 we present an example of “how to find” the inverse frame to aGabor frame. In Section 1.10.6.4 a condition for constructing Gabor frames in discrete spaces from itscontinuous counterpart is stated.

1.10.5.5 Wavelet framesWavelet analysis asks if translated and scaled versions of functionψ(t) can be employed for representinga signal s(t) ∈ L(R). In this sense, the signal is to be represented using functions like

ψc,a(t) = 1√cψ

(t − a

c

). (10.66)

If one thinks about the signal projections over different ψc,a(t) to represent the signal, the similaritywith the Gabor “time-frequency” approach is unavoidable. One applies operations to a fixed prototypefunction and use the resulting modified versions of the prototype to express the signal. Discrete pairs(a, c) define how the prototype function has been modified and thus provide what signal characteristicsare being analyzed.

If one employs a continuum of (a, c) pairs, one has the so-called Continuous Wavelet Transform[4,5,7,50]

Wψ(a, c) = 〈 f (t), ψc,a(t)〉 =∫

f (t)1√cψ

(t − a

c

)dt . (10.67)

One can compute the inverse wavelet transform using

f (t) = Kψ

∫ ∞

0

∫Wψ(a, c)

1√cψ

(t − a

c

)da

dc

c2 (10.68)


In the equations above, we have considered that ψ(t) is real and Kψ is a constant that depends on ψ(t)[4,51,52]. It is worthy to say that, similarly, the Gabor frame can be linked to the so-called Short TimeFourier Transform [4,21].

In the case of wavelet frames, we are concerned with the case of a discrete set of pairs (a, c). In thissense in 1910 [53], Haar has shown that for the function

ψ(t) =⎧⎨⎩

1, 0 ≤ t < 1/2,1, 1/2 ≤ t < 1,0, otherwise

(10.69)

the set of dilated and translated versions

ψ j,k(t) = 2 j/2ψ(

2 j t − k), j, k ∈ Z,

what means that ψ j,k(t) = Tk D2− j { f (t)} (10.70)

is a basis for L2(R).A more throughout study of such possibilities occurred in the beginning of the 1980s. Calderon in

1964 [51] and Grossman and Morlet in 1985 [52] introduced the Wavelet Transform. In continuation,the first wavelet frame construction appeared in 1986 in [12]. Great effort was dedicated to the study ofwavelets due to their multi-resolution analysis [4,5,7,13,50]. A recent book [54] collects some relevantpapers for wavelet analysis theory.

One should note that in Eqs. (10.66)–(10.70), the order in which the translation and dilation operationare applied differs from the one previously presented in Section 1.10.5.2. Therefore, for clarity, we nowdefine what is meant by a Wavelet Frame.

Definition 10.13. For c > 1, a > 0 and g ∈ L2(R), a frame constructed by dilations and translations as{Tnac j Dc j g(t)

}j,n∈Z

={

cj2 g(

c j t − na)}

j,n∈Z(10.71)

is called a wavelet frame. �In [5] both necessary and sufficient conditions are provided to construct wavelet frames. For exam-

ple, if{Tnac j Dc j g(t)

}j,n∈Z

={

cj2 g(c j t − na

)}j,n∈Z

is a frame with frame bounds A and B then

necessarily [55]

A ≤ 1

a

∑j∈Z

∣∣∣g (c jω)∣∣∣2 ≤ B, (10.72)

where g(ω)

is the Fourier transform of g(t).A sufficient condition to generate a wavelet frame [6] is: suppose that c > 1, a > 0 and g(t) ∈ L2(R)

are given, if

B := 1

bsup

|ω|∈[1,c]

∑j,n∈Z

∣∣∣g (c jω)

g(

c jω + n

a

)∣∣∣ < ∞, and (10.73)

A := 1

binf|ω|∈[1,c]

⎡⎣∑n∈Z

∣∣∣g (c jω)∣∣∣2 −

∑n �=0

∑j∈Z

∣∣∣g (c jω)

g(

c jω + n

a

)∣∣∣⎤⎦ > 0 (10.74)


Localized Sinusoid

Localized Phenomenon

Localized Damped Sinusoid

FIGURE 10.3

Examples of fixed functions that can be used to construct frames.

then {Tnac j Dc j g(t)} j,n∈Z is a frame for L2(R) with bounds A, B given by the expressions above.In [49] an extension of this condition for irregularly sampled time-scale parameters, when the set(

an, c j)

is replaced by any pair(an, j , cn, j

) ∈ [c j an, c j a(n + 1

)] × [c j , c j+1], is provided.

1.10.5.6 Finite vector spacesWhen considering finite vector spaces H

N the simpler solution is to truncate or box-window the elementsof the discrete space frame; however, this simple approach alters the frame bounds [56]. An alternativeis to consider that the vector space is generated from an N-length section of an N-periodic l2(Z) space,where the translation operator is a circular shift. The circular shift of a signal does not change the vectornorm; this way the frame in H

N has the same frame bounds as the frame in the N-periodic l2(Z).

1.10.6 Some remarks and highlights on applications1.10.6.1 Signal analysisWe have discussed the construction of different frames from a fixed prototype function. We havealso presented examples that span the possibility of signal representation, analysis and synthesis usingfunctions with prescribed characteristics. These are some of the main features of frames that are relevantfor signal processing applications: detection of signal features, analyzing and synthesizing signals usinga set of pre-defined signals. Frames bring freedom when compared to basis as the number of elementsto be employed in the analysis-synthesis process can be larger than the signal space dimension.

In the literature, there is a large number of examples of different frame constructions using a fixedprototype signal; we have presented some in previous sections. Figure 10.3 illustrates different functionswith different characteristics that can be used in this framework. These figures illustrate possible fixedfunctions to be employed to detect localized phenomena in a signal.

1.10.6 Some Remarks and Highlights on Applications 581

Gabor Frame Elements Heisenber Boxes

Time

Wavelet Frame Elements Heisenber Boxes

TimeFr

eque

ncy

Freq

uenc

y

FIGURE 10.4

Heisenberg boxes in the time-frequency plane for Gabor (left) and wavelet (right) frames.

The Gabor and wavelet approaches commonly used for building frames from a fixed function werediscussed. In some cases, the density in time, frequency or scale of the phenomena to be analyzed ordetected in the signal [24,25,57] may be such that techniques for reducing the number of frame elementsmay be handy [58].

Example 10.10. As the reader may have already noticed, Wavelet frames can be used in a similar wayto Gabor frames for time-frequency analysis. However, in this case a better phrasing would be time-scale analysis. Figure 10.4 depicts the different tilings of the time-frequency plane obtained by theseapproaches. These tilings depict the Heisenberg boxes [4] of the frame elements—these boxes roughlycorrespond to the time-frequency plane area where most of the elements’ energy is concentrated. �

1.10.6.2 GaborgramIn the beginning of Section 1.10.5.4 we have presented the idea of Gabor. That was representing a signals(t) by means of

s(t) =∑

m

∑n

cm,n EmbTnag(t) =∑

m

∑n

cm,ne jmbg(t − na). (10.75)

As it can be noticed, this equation assumes that there is a representation for any s(t) using severalgmb,na(t) = EmbTnag(t). We have seen that for these to be true it is enough for the set {EmbTnag(t)}m,nto be a frame. What is commonly referred to as the Gaborgram is the plot of the coefficients cm,n using thelattice presented in Figure 10.2. As discussed above a similar concept can be used for Wavelet Frames.


We have also seen that to compute the{cm,n

}m,n , the inverse frame to {EmbTnag(t)}m,n must be used.

The next section illustrates how to obtain the inverse frame in the case of an exponential function. AGabor frame of damped sinusoids is inspired on the connection among transients and damped sinusoidsin physical systems.

1.10.6.3 Inverse gabor frameThe power of Gabor system for signal analysis and synthesis is not difficult to perceive. The problemis how to find the coefficients cm,n that provide the reconstruction

s(t) =∑

m

∑n

cm,n EmbTnag(t) =∑

m

∑n

cm,ne jmbg(t − na). (10.76)

The inverse frame elements {g(t)}m,n can be used. In this case one has that

s(t) =∑

m

∑n

〈c, gm,n(t)〉EmbTnag(t). (10.77)

Daubechies [13] has shown that, for Gabor frames, the inverse frame must be such that

gm,n(t) = EmbTna g(t). (10.78)

And one has thats(t) =

∑m

∑n

〈c, EmbTna g(t)〉EmbTnag(t). (10.79)

However, one still has to find g(t). Equation (10.79) provides a biorthogonality condition (which isthe “frame decomposition” formulas discussed in Section 1.10.3.1). In that sense, in [59] it is shownthat Eq. (10.79) leads to

1

ab

∫g(t)g

(t − n

b

)e− j2πm t

a dt = δ(n)δ(m), (10.80)

where δ(x) denotes the impulse of x. In [59] it is also discussed that Eq. (10.80) accepts more than onesolution in the oversampled case (ab < 1) [27].

Example 10.11. In [24] the problem of building a Gabor system/frame based on the one sidedexponential is presented. In this case, one has that

g(t) ={√

2αe−αt , t ≥ 00, otherwise

(10.81)

Using the Zak Transform [47,60] in [24] it is derived that for large oversampling (ab < 1)

g(t) ≈

⎧⎪⎨⎪⎩−ke−α(

t+ 2b

), −1 ≤ tb < 0

ke−αt , 0 ≤ tb < 10, otherwise

(10.82)

Where k is a constant that depends on a, b and α [24].Figure 10.5 illustrates the g(t) for different cases of a and b. In order to just focus on the waveform of

g(t), we ignore here the value of the constant k and make all functions scaled so that maxt g(t) = 1.�


1.10.6.4 Gabor frames in discrete spacesObviously, due to the time-shift frequency-shift structure of Gabor frames, these have a broad rangeof applications for signal processing. Since most algorithms take place in a discrete space we shouldevaluate how to construct Gabor frames in this space.

Frames in discrete spaces (l2(Z)) can be obtained by time-sampling the elements of frames in L2(R)

[6]. If g(t) ∈ L2(R) is such that

limε→0

∑k∈Z

1

ε

∫ 12 ε

− 12 ε

|g(k + t)− g(k)|2dt = 0, (10.83)

and if {Em/Q Tn/P g(t)}m,n∈Z with Q, P ∈ N is a frame for L2(R) with frame bounds A and B, then{Em/Q Tn/P gD}n∈Z,m=0,1,...,M−1 is a frame for l2(Z) with frame bounds A and B [6]. The vector gD isthe discretized version of g(t) with one sample per time unit, i.e., gD = {g( j)} j∈Z.

1.10.6.5 Fast analysis and synthesis operators for gabor framesGabor or Weyl-Heisenberg frames in N-dimensional spaces are also interesting because one can findfast algorithms to compute their analysis and synthesis operators. Now, we consider that the numberof “points” of the Weyl-Heisenberg frame in the frequency axis is such that Q = N/r , Q, r ∈ N and

0 1 2Time

b=1, α =1

−2 −1−2 −1

−2 −1

0 1 2

0

0.5

1

Time

b=1, α =4

0 1 2Time

b=1, α =1/2

−0.5 0 0.5−1

0

1

0

0.5

1

−1

0

1

Time

b=4, α =1/4

FIGURE 10.5

Inverse Gabor frame prototype function of the one sided exponential—Biorthogonal function g (t) to g (t) =√2αe−αt u(t), for different values of α.


also that the number of points in the time axis P ∈ N (the same restrictions were employed in Section1.10.6.4 above. These restrictions actually lead to a = 1/Q and b = N/P in Eq. (10.59). In this case,the cn,m are defined for n ∈ {0 . . . P − 1} and m ∈ {0 . . . Q − 1} and are given by

cn,m = 〈x, Em 1Q

Tn NP

g〉 =N−1∑l=0

x[l]e − j2πlmQ Tn N

Pg[l]. (10.84)

Defining

fn NP[k] = x[k]Tn N

Pg[k] = x[k]g

[(k − n

N

P

)modN

](10.85)

fn NP

=[fn N

P[0], . . . , fn N

P[N − 1]

](10.86)

we obtain

cn,m = 〈x, Em 1Q

Tn NP

g〉 =N−1∑l=0

fn NP[l]e − j2πlm

Q . (10.87)

For Q = N/r , r ∈ N we obtain

cn,m = 〈x, Em 1Q

Tn NP

g〉 =N−1∑l=0

fn NP[l]e − j2πl

N mr = DFT {fn NP}[mr ], (10.88)

where DFT {x}[k] is the kth sample of the Discrete Fourier Transform of x, which can be computedusing fast algorithms (FFT) [31].

Using the result in Eq. (10.88) the analysis operator (which was discussed in Section 1.10.4.2) ofsuch Weyl-Heisenberg frames is given by

T∗{·} : HN → C

PQ, (10.89)

T∗{x} : cn,m = {〈x, Em 1Q

Tn NP

g〉}, n ∈ [0, P − 1],m ∈ [0, Q − 1] (10.90)

cn,m = F FT {fn NP}[mr ], fn N

P[k] = x[k]g

[(k − n

N

P

)modN

](10.91)

Similarly, for the synthesis operator of such frames we have that

T{·} : CPQ → H

N ,

T{cn,m} = [t[0], . . . , t[N − 1]] =P−1∑n=0

Q−1∑m=0

cn,m Em 1Q

Tn NP

g. (10.92)

The last equation is the sum of PQ N-length vectors and can be computed using the Inverse DiscreteFourier Transform as below

t[l] =P−1∑n=0

Q−1∑m=0

cn,me j 2πmlQ fn N

P[l] =

P−1∑n=0

fn NP[l]

Q−1∑m=0

cn,me j 2πN lmr . (10.93)


Denoting cn(m) = cn,m we can define

cn = [cn[0], cn[1], . . . , cn[Q − 1]] , (10.94)

and its up-sampled version

U (cn)[k] ={

cn[k/r ], k/r ∈ {0, 1, . . . , Q − 1}0, otherwise

(10.95)

Thus, the synthesis operator is such that

t[l] =P−1∑n=0

fn NP[l]

Q−1∑m=0

U (cn)[mr ]e j 2πN lmr

=P−1∑n=0

fn NP[l]I F FT {U (cn)}[l]. (10.96)

Figure 10.6 presents a way to compute the expansion coefficients or the reconstructed signal when aframe is used either as an analysis or as a synthesis operator. Note that in the outputs of each FFT block, inFigure 10.6, there is a serial to parallel converter, while at the IFFT inputs a parallel to serial converterexists. Figure 10.6 shows that all the frame coefficients can be obtained using P N (1 + log2 (N ))operations, which can be reduced if one takes into account that just Q FFT/IFFT coefficients need to becomputed at each FFT/IFFT branch.

1.10.6.6 Time-frequency content analysis using frame expansionsAs we have seen in several cases, frames provide a powerful tool for signal analysis. This is speciallytrue when one considers Gabor and Wavelet frames. These provide a natural tiling of the time-frequencyplane. We now discuss how can a signal expansion be mapped into a time-frequency plot.

The Wigner-Ville distribution (WD) is probably one of the most well-known and widely used toolsfor time-frequency analysis of signals. The WD of a signal x(t) is defined as [4,18,20,61]

W Dx(t, f

) =∫ +∞

−∞x(

t + τ

2

)x∗ (t − τ

2

)e−2π j f τdτ, (10.97)

Some applications have used the WD of signal decompositions in order to analyze the time-frequencycontent of signals [4,20]. The Wigner-Ville distribution of a signal x(t),W Dx (t, f ), is a “measure”of the energy density in the signal in both time (t) and frequency (f) simultaneously. However, it isjust meaningful when regions of the time-frequency plane are considered, that is as local averages,and it can not be considered at a given time-frequency point (t ′, f ′) due to uncertainty principle [4,20].In addition, the WD of a signal has another drawback since it is not restricted to be positive, a mandatorycharacteristic for an energy density.

In Section 1.10.3, we have seen that a signal x can be decomposed into a frame {gk}k∈Kor into itsdual {gk}k∈K, and reconstructed by means of

x =∑k∈K


〈x, gk〉gk . (10.98)


FIGURE 10.6

Weyl-Heisenberg fast analysis and synthesis using FFTs and IFFTs.

Once x is decomposed into the frame coefficients 〈x, gk〉, its time-frequency content can be analyzedusing

W Dx (t, f ) =∑k∈K

〈x(t), gk(t)〉 W Dgk

(t, f

). (10.99)

Using approaches like this, the inference of signal characteristics from signal decompositions is acommon and powerful tool for signal analysis, detection and estimation. Specifically, in the case oftime-frequency analysis, such approaches avoids the cross terms that appear when the original signal isdirectly analyzed [4,18–22,42,61].

However, as we have seen the coefficients 〈x(t), gk(t)〉 used above may be obtained with a gk(t) thatdoes not, neither physically, nor visually and nor in its time-frequency content, resembles the associatedgk(t). That is why in general this approach is left aside for others.

For instance, in [42] the time-frequency content of signals is estimated using the Matching Pur-suits (MP) signal decomposition algorithm - a step based decomposition algorithm that iterates forsuccessively obtaining better signal approximations [62]. The approximation is accomplished by

1.10.7 Conclusion 587

selecting the dictionary element that best matches the signal. This match is evaluated by projectingthe signal on the dictionary elements. The best matching element is scaled by its correspondent projec-tion and subtracted from the signal generating a residual error. The process is then applied to the residualerror iteratively. This algorithm is considered to be greedy [63–65], in the sense that it minimizes theapproximation error at each iteration. However, this may not be optimal as a sequence of iterationsoccur, and due to this fact several variants for the MP have been presented [65–68].

Although the dictionary employed in the MP is not required to be a frame, it has been a commonprocedure to employ a frame [58,69,70] as a dictionary. This is so because this choice guarantees theanalysis and synthesis of any signal. In general, a mix of Gabor and Wavelet frames is employed tobuild the dictionary used together with the MP [42]. Doing this, the dictionary elements are placed indifferent locations in the time-frequency plane and the several scales provide different concentrationsfor the energy of the elements in time and frequency. It is common to employ a dictionary formed byGaussian functions in different scales with different time and frequency shifts. One then obtains the MPexpansion of signal x(t) into the Gabor dictionary

x(t) ≈M∑

n=1

γngi(n)(t), (10.100)

where the i(n) indexes the element selected for approximating the residual error in the decompositionstep n. From this representation the time-frequency content of x(t) is analyzed by means of

M∑n=1

γnW Dgi(n) (t, f ). (10.101)

In [71] the same approach is used, but using a modified version of the MP to obtain the signal expan-sion. Note the resemblance between the approach in Eq. (10.99) and the ones in Eqs. (10.100) and(10.101).

1.10.7 ConclusionIn this chapter we have made an overview of frames. We started by introducing the concept of overcom-plete decompositions, highlighting its main characteristics. We then commented on the dual frames,and how frames can be employed to perform analysis and synthesis of signals. After this, we pro-vided formal definitions of the frame operator, inverse frames and frame bounds. We then formalizedthe use of frames in discrete and finite dimensional spaces. Shifting into more practical matters, weshowed how to generate frames from a prototype function, by using translations, modulations and dila-tions. From this, we analyzed the widely used frames of translates, Gabor frames and wavelet frames.We then described some applications in signal analysis, including the Gaborgram, and presented away to compute it more efficiently using the Fast Fourier Transform. We finished the chapter by pre-senting ways to compute time-frequency analysis using decompositions and the matching pursuitsalgorithm.


Relevant Theory: Signal Processing Theory

See this Volume, Chapter 2 Continuous-Time Signals and SystemsSee this Volume, Chapter 3 Discrete-Time Signals and SystemsSee this Volume, Chapter 5 Sampling and QuantizationSee this Volume, Chapter 7 Multirate Signal Processing for Software Radio ArchitecturesSee this Volume, Chapter 8 Modern Transform Design for Practical Audio/Image/Video CodingApplicationsSee this Volume, Chapter 9 Discrete Multi-Scale Transforms in Signal Processing

References[1] R.J. Duffin, A.C. Schaeffer, A class of nonharmonic fourier series, Trans. Am. Math. Soc. 72 (1952) 341–366.[2] R. Young, An Introduction to Nonharmonic Fourier Series, Academic Press, New York, 1980.[3] R. Paley, N. Wiener, The fourier transforms in the complex domain, Am. Math. Soc. Colloqium Publ. Ser. 19

(1934).[4] S. Mallat, A Wavelet Tour of Signal Processing, first ed., Academic Press, San Diego, California, USA, 1998.[5] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Philadelphia,

Pennsylvania, USA, 1991.[6] O. Christensen, An Introduction To Frames And Riesz Bases, Applied and Numerical Harmonic Analysis,

first ed., Birkhuser, Boston, Basel, Berlin, 2002.[7] C.S. Burrus, R.A. Gopinath, H. Guo, Introduction To Wavelets and Wavelets Transforms A Primer, first ed.,

Prentice Hall, Upper Saddle River, New Jersey 07458, USA, 1998.[8] Z. Cvetkovic, M. Vetterli, On simple oversampled a/d conversion in l2(R), IEEE Trans. Inform. Theory. 47

(1) (2001) 146–154.[9] F. Marvasti (Ed.), Nonuniform Sampling: Theory and Practice, first ed., Springer, 2001.

[10] M. Vetterli, P. Marziliano, T. Blu, Sampling signals with finite rate of innovation, IEEE Trans. Signal Process.50 (6) (2002) 1417–1428.

[11] R.G. Baraniuk, Compressive sensing [lecture notes], IEEE Signal Process. Mag. 24 (4) (2007) 118–121.[12] I. Daubechies, A. Grossman, Y. Meyer, Painless nonhorthogonal expansions, J. Math. Phys. 27 (1986) 1271–

1283.[13] I. Daubechies, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inform.

Theory. 36 (5) (1990) 961–1005.[14] C. Heil, D. Walnut, Continuous and discrete wavelet transforms, SIAM Review 31 (1989) 628–666.[15] V.K. Goyal, J. Kovacevic, J.A. Kelner, Quantized frame expansions with erasures, Appl. Comput. Harmon.

Anal. 10 (2001) 203–233.[16] P.G. Casazza, J. Kovacevic, Equal-norm tight frames with erasures, Adv. Comput. Math.–Especial ISSUE on

Frames (2002) 387–430.[17] J. Kovacevic, P.L. Dragotti, V.K. Goyal, Filter bank frame expansions with erasures, IEEE Trans. Inform.

Theory. 48 (6) (2002) 1439 –1450.[18] G. Matz, F. Hlawatsch, Wigner distributions (nearly) everywhere: Time-frequency analysis of signals, systems,

random processes, signal spaces, and frames, Elsevier Signal Process. 83 (2003) 1355–1378.[19] L. Cohen, Time-frequency distributions—a review, Proc. of the. IEEE 77 (7) (1989) 941–981.[20] F. Hlawatsch, G.F. Boudreaux-Bartels, Linear and quadratic time-frequency signal representations, IEEE

Signal Process. Mag. 9 (1992) 21–67.

References 589

[21] Leon Cohen, Time-Frequency Analysis, Prentice Hall Signal Processing Series. Prentice Hall, Prentice HallPTR, Englewood Cliffs, New Jersey 07632, 1995.

[22] Kalheiz Grchenig, Foundations of Time-Frequency Analysis, Birkhuser, New York, USA, 2001.[23] D. Gabor, Theory of communications, IEEE J. 93 (1946) 429–457.[24] B. Friedlander, A. Zeira, Oversampled gabor representation for transient signals, IEEE Trans. Signal Process.

43 (9) (1995) 2088–2094.[25] B. Friedlander, B. Porat, Detection of transient signals by the gabor representation, IEEE Trans. Acoust.

Speech Signal Process. 37 (2) (1989) 169–180.[26] M. Zibulski, Y.Y. Zeevi, Discrete multiwindow gabor-type transforms, IEEE Trans. Signal Process. 45 (1997)

1428–1442.[27] M. Zibulski, Y.Y. Zeevi, Oversampling in the gabor scheme, IEEE Trans. Signal Process. 43 (9) (1993)

2679–2687.[28] D.L. Donoho, M. Vetterli, R.A. DeVore, I. Daubechies, Data compression and harmonic analysis, IEEE Trans.

Inform. Theory. 44 (6) (1998) 2435–2476.[29] E.J. Candes, D.L. Donoho, Ridgelets: a key to higher-dimensional intermittency?, Philos. Trans.: Math., Phys.

Eng. Sci. 357 (1999) 2495–2509.[30] D.L. Donoho, A.G. Flesia, Can recent innovations in harmonic analysis ‘explain’ key findings in natural image

statistics?, Network: Computation in Neural Systems, Taylor & Francis, vol. 12, pp. 371–393, 2001.[31] P.S.R. Diniz, E.A.B. da Silva, S. Lima Netto, Digital Signal Processing: System Analysis and Design, Cam-

bridge University Press, 2001.[32] T. Strohmer, R.W. Heath Jr., Grassmannian frames with applications to coding and comunications, App.

Comput. Harmon. Anal. 14 (3) (2003) 257–275.[33] Z. Cvetkovic, Martin Vetterli, Oversampled filter banks, IEEE Trans. Signal Process. 46 (5) (1998) 1245–1255.[34] H. Bölcksei, F. Hlawatsh, H.G. Feichtinger, Frame-theoretic analysis of oversampled filter banks, IEEE Trans.

Signal Process. 46 (12) (1998) 3256–3268.[35] V.K. Goyal, M. Vetterli, N.T. Thao, Quantized overcomplete expansions in R

N : Analysis, synthesis, andalgorithms, IEEE Trans. Infor. Theory. 44 (1) (1998) 16–31.

[36] N J Munch, Noise reduction in tight weyl-heisenberg frames, IEEE Trans. Inform. Theory. 38 (2) (1992)608–616.

[37] J.A. Tropp, I.S. Dhillon, R.W. Heath Jr., T. Strohmer, Designing structured tight frames via an alternatingprojection method, IEEE Trans. Inform. Theory. 51 (2005) 188–209.

[38] Y.C. Eldar, G.D. Forney Jr., Optimal tight frames and quantum measurement, IEEE Trans. Inform. Theory.48 (3) (2002) 599–610.

[39] Y.C. Eldar, H. Bolcskei, Geometrically uniform frames,” IEEE Trans. Inform. Theory. 49 (4) (2003) 993–1006.[40] J.J. Benedetto, M. Fickus, Frame potentials, Adv. Comput. Math 18 (2003) 357–385.[41] R. Vale, S. Waldron, Tight frames and their symmetries, Constr. Approx. 21 (2005) 83–112.[42] S. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Trans. Signal Process. 41 (12)

(1993) 3397–3415.[43] A.J. Jerri, The shannon sampling theorem—its various extensions and applications: A tutorial review, Proc.

IEEE 65 (11) (1977) 1565–1596.[44] R.J. Marks II, Introduction to Shannon Sampling and Interpolation Theory, Springer-Verlag, 1991.[45] J. von Neumann, Mathematische Grundlagen der Quantenmechanik, Springer Berlin, 1932, English version:

Mathematical Foundations of Quantum Mechanichs, Princeton Univ. Press, 1955.[46] S. Qian, D. Chen, Joint time-frequency analysis, IEEE Signal Process. Mag. (1999) 52–67.[47] A.J.E.M. Janssen, Zak Transforms With Few Zeros and The Tie, in: H.G. Feichtinger, T.Strohmer (Eds.),

Advances in Gabor Analysis, Birkhuser, Boston, 2002.


[48] P.G. Casazza, O. Christensen, Weyl-heisenberg frames for subspaces of L2(R), Proc. Am. Math. Soc. 129(2001) 145–154.

[49] W. Sun, X. Zhou, Irregular wavelet/gabor frames, Appl. Comput. Harmon. Anal. 13 (2002) 63–76.[50] Mladen Victor Wickerhauser, Adapted Wavelet Analysis from Theory to Software, IEEE Press, Piscataway,

NJ, USA, 1998.[51] A.P. Calderon, Intermediate spaces and interpolation, the complex method, Studia Math. 24 (1964) 113–190.[52] A. Grossmann, J. Morlet, Decomposion of hardy functions into square integrable wavelets of constant shape,

SIAM J. Math. Anal. 15 (1984) 723–736.[53] A. Haar, Zur theorie der orthogonalen funktionensysteme, Math. Ann. 69 (1910) 331–371. Translated Version:

On the theory of orthogonal function systems, by G Zimmermann.[54] Christopher Heil, David F. Walnut, Fundamental Papers in Wavelet Theory, Princeton University Press, 2006.[55] C. Chui, X. Shi, Inequalities of Littlewod-Paley type for frames and wavelets, Siam J. Math. Anal. 24 (1993)

263–277.[56] T. Strohmer, Numerical analysis of the non-uniform sampling problem, Comput. Appl. Math. 122 (2000)

297–316.[57] R. Balan, I. Daubechies, V. Vaishampayan, The analysis and design of windowed fourier frame based multiple

description source coding schemes, IEEE Trans. Inform. Theory. 46 (2000) 2491–2536.[58] K. Engan, S.O. Aase, J.H. Husoy, Designing frames for matching pursuits algorithms, in: IEEE International

Conference on Acoustics, Speech, and Signal Processing, May 1998, pp. 1817–1820.[59] J. Wexler, S. Raz, Discrete gabor expansions, Signal Process. 21 (3) (1990) 207–221.[60] J. Zak, Finite translations in solid-state physics, Phys. Rev, Lett. 19 (1967) 1385.[61] T. Claasen W. Mecklenbrauker, The aliasing problem in discrete-time wigner distributions, IEEE Trans.

Acoust. Speech Signal Process. 31 (1983) 1067–1072.[62] Lisandro Lovisolo, Eduardo A.B. da Silva, Paulo S.R. Diniz, On the statistics of matching pursuit angles,

Signal Process. 90 (2010).[63] R.A. DeVore, V.N. Temlyakov, Some remarks in greedy algorithms, Adv. Comput. Math. 5 (1996) 173–187.[64] G. Davis, S. Mallat, M. Avellaneda, Adaptive greedy approximations, J. Constr. Approx. 13 (1997) 57–98.[65] J.A. Tropp, Greed is good: algorithmic results for sparse approximation, IEEE Trans. Inform. Theory. 50

(2004) 2231–2241.[66] Y.C. Pati, R. Rezaiifar, P.S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation

with applications to wavelet decomposition, in: IEEE Twenty-Seventh Asilomar Conference on Signals,Systems and Computers, 1993.

[67] J.A. Tropp, A.C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit, IEEETrans. Inform. Theory. 53 (2007) 4655–4666.

[68] L. Rebollo-Neira, D. Lowe, Optimized orthogonal matching pursuit approach, IEEE SignalProcess. Lett. 9 (2002) 137–140.

[69] L. Lovisolo, M.A.M. Rodrigues, E.A.B. da Silva, P.S.R. Diniz, Efficient coherent decompositions of powersystems signals using damped sinusoids, IEEE Trans. Signal Process. 53 (2005) 3831–3846.

[70] P.J. Durka, D. Ircha, K.J. Blinowska, Stochastic time-frequency dictionaries for matching pursuit, IEEE Trans.Signal Process. 49 (2001) 507–510.

[71] A. Papandreou-Suppappola, S.B. Suppappola, Analysis and classification of time-varying signals with multipletime-frequency structures, IEEE Signal Process. Lett. 9 (2002) 92–95.

Documents

[Academic Press Library in Signal Processing] Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning Volume 1 || Frames in Signal Processing