Vanishing Moments of Wavelets on -adic Fields
Abstract
A study on the vanishing moments of wavelets on -adic fields is carried out in this paper. The -vanishing moments and discrete -vanishing moments are defined on a -adic field and the relation between them is investigated. The -vanishing moments of Haar-type and non-Haar type wavelet functions are computed. Also, the connection between -vanishing moment of non-Haar type wavelet functions and the approximation order of the indicator function of the compact open subgroup of are established. A characterization of the -vanishing moments of nonorthogonal wavelets is done.
Department of Mathematics
National Institute of Technology Calicut
NIT Campus P O - 673 601, India
∗nathira.1996@gmail.com
†lineesh@nitc.ac.in
keywords: -adic field; pseudo-differential operator; refinable function; p-adic wavelets; approximation order; vanishing moment; compact support.
AMS Subject Classification 2020: 11E95, 11F85, 41A10, 42C40, 43A70.
1 Introduction
Over the past few years, wavelets have gained immense popularity in almost all fields of science and technology due to their unique time-frequency localization feature. One of the most significant facts is that, in addition to the canonical tool of representing a function by its Fourier series, there is a different representation using wavelets which is more suitable to certain problems in data compression and signal processing. The first wavelet construction is due to Alfréd Haar in 1910. Then many mathematicians including Jean Morlet, Alex Grossmann, Yves Meyer, Stephane Mallat and Ingrid Daubechies have contributed various kinds of wavelets to theoretical and applied science. The concept of wavelet is then extended to the Euclidean space as well as many other topological spaces such as -adic fields.
Wavelets can be generated from scaling functions as well as wavelet sets. In their paper, Albeverio et al.[1, 2] proposed a complete characterization of scaling functions and explained what types of scaling functions form -adic multiresolution analysis. Khrennikov et al. [3] described the procedure to construct -adic wavelets from -adic scaling functions associated with an expansive automorphism. The authors [4] discussed about all compactly supported orthogonal wavelet bases for generated by the unique p-adic multiresolution analysis, i.e.,the Haar bases of .
In 2008, Shelkovich and Skopina [5] constructed infinitely many different multidimensional -adic Haar orthonormal wavelet bases for and in 2009, Khrennikov and Shelkovich [6] developed infinite family of compactly supported non-Haar type -adic wavelet bases for . Kozyrev et al. [7] also discussed about the one-dimensional and multi-dimensional wavelet bases and their relation to the spectral theory of pseudo-differential operators.
Refinable functions play an important role in the construction and properties of wavelets. Basically, most of the wavelets are generated from refinable functions. In their paper Athira and Lineesh [8] discussed about the approximation order of shift-invariant space of a refinable function on p-adic field. The relation between the approximation order, accuracy of refinable function and order of the Strang–Fix condition are also established.
In order to fulfill the requirements, some properties are relevant for the wavelet bases. One of the important property among them is the vanishing moment. Vanishing moments are essential in the context of compression of a signal. A vanishing moment limits the wavelet’s ability to represent polynomial behaviour or information in a signal. Higher vanishing moments of wavelets are required for signal compression and denoising. In 1999, S. Mallat [9] established that the number of vanishing moments of a wavelet and the approximation order of the corresponding scaling function are equivalent in . Then Di-Rong Chen et al. [10] obtained the the relation between the order of sum rules and the number of vanishing moments of wavelets on . An explicit formula for refinement masks providing vanishing moments is explored by Skopina[11]. In 2010, Yu Liu and L. Peng [12] proved the connection between discrete vanishing moments and sum rules on the Heisenberg group.
Our goal is to extend the concept of vanishing moments to the -adic field . Section 2 contains preliminary informations about scaling functions, wavelet functions and accuracy of scaling functions on . In section 3, published results about the relationship between accuracy and vanishing moments on Euclidean spaces are discussed. The definitions of -vanishing moments of compactly supported functions on and discrete -vanishing moments of finitely supported sequences on are given in section 4. The relationship between the -vanishing moments and discrete -vanishing moments are also established in this section. In section 5, the -vanishing moments of Haar-type wavelet functions are calculated. The -vanishing moments of non-Haar type wavelet functions are computed in section 6. In this section, we proved the connection between -vanishing moment of non-Haar type wavelet functions and the approximation order of the indicator function of the compact open subgroup of . Finally in section 7, we characterized the -vanishing moments of nonorthogonal wavelets.
2 Preliminary
Let be a prime number. Consider the completion field of with respect to the norm defined by,
where not divisible by . Denote the above field as . The canonical form of , is,
| (2.1) |
where . Then for this , the fractional part of is,
The dual group of is itself and the character on is defined as,
| (2.2) |
where is the fractional part of a number. Denote
Then is a compact open subgroup of . Let be the Haar measure on with . Denote by . Let be the collection of all integrable functions such that . The Fourier transform of a complex-valued function defined on is defined as
If is a measurable subset of and , then denotes the quantity . If , then denotes the essential supremum of over .
For the -adic analysis related to the mapping , the operation of differentiation is not defined. An analogy of the differentiation operator is a pseudo-differential operator. The pseudo-differential operator is defined [13] by
| (2.3) |
where with .
Remark 2.1.
[13] The derivative , is given by the expression
| (2.4) |
Remark 2.2.
[13] For and , .
Remark 2.3.
[13] For , let . Then
A polynomial on is given by
where each . If , then the degree of is . For a nonnegative integer , we denote by the linear span of . Then is the linear space of all polynomials.
Let us consider the set
Then there is a “natural” decomposition of into a union of mutually disjoint discs: . So, is a “natural set” of shifts for .
We denote by the linear space of all sequences on , and by the linear space of all finitely supported sequences on .
For a compactly supported function on and a sequence , the semi-convolution of with is defined by
| (2.5) |
Let denote the linear space . We call the shift-invariant space generated by .
Define by . Then, is an expansive automorphism with modulus, and .
Definition 2.1.
[2] A collection of closed spaces , is called a multiresolution analysis(MRA) in if the following axioms hold:
-
1.
for all ;
-
2.
is dense in ;
-
3.
;
-
4.
for all ;
-
5.
there exists a function such that is an orthonormal basis for .
The function from axiom is called scaling function. Then we says that a MRA is generated by its scaling function (or generates the MRA). It follows immediately from axioms and that the functions , form an orthonormal basis for . Let be an orthogonal scaling function for a MRA , then
| (2.6) |
Such equations are called refinement equations, and their solutions are called refinable functions.
Generally, a refinement equation (2.6) does not imply the inclusion property because the set of shifts does not form a group. Indeed, we need all the functions , to belong to the space , i.e., the identities should be fulfilled for all . Since is not in in general, we cannot argue that belongs to for all . Thus the refinement equation must be redefined as in the following definition.
Definition 2.2.
Denote by the set of all -periodic functions supported on . Then we have the following results about refinable functions [2].
Theorem 2.1.
A function , with generates a MRA if and only if
-
1.
is refinable;
-
2.
there exists at most integers such that and .
Theorem 2.2.
Let be defined by (2.9), where is the trigonometric polynomial (2.10) with . If for all not divisible by , then . If furthermore, for all divisible by , then is an orthonormal system. Conversely, if and the system is orthonormal, then whenever is not divisible by , whenever is divisible by , , and for any .
Theorem 2.3.
[2] There exists a unique MRA generated by an orthogonal scaling function.
Remark 2.4.
That is all orthogonal scaling functions generate the same Haar MRA.
Suppose we have a p-adic MRA generated using a scaling function satisfying the refinement equation (2.7), then the wavelet functions are in the form
| (2.11) |
where the coefficients are chosen such that
| (2.12) |
for any .
Set
and
In order to satisfy (2.12), we need to find such that the matrix
| (2.13) |
is unitary.[3]
Let be a compactly supported function in . The norm in is denoted by . For an element and a subset of , the distance from to , denoted by , is defined by
Let . For , let , where . For a real number , we say that provides approximation order if for each sufficiently smooth function , there exists a constant such that
The relation between the approximation order provided by , the order of the Strang–Fix condition and the accuracy of are established in the following theorems.
Theorem 2.4.
[8] Let , and let be a compactly supported function in with . For every positive integer , the following statements are equivalent:
-
1.
provides approximation order .
-
2.
contains .
-
3.
.
Definition 2.3.
[8] For a compactly supported function on , define
3 Vanishing Moments on Euclidean spaces
In this section we recall some facts about vanishing moments on Euclidean spaces.
Let be a wavelet function on . Then we say that has vanishing moments if
A wavelet with vanishing moments is orthogonal to each of the polynomials of degree [9].
The following theorem gives the relationship between the approximation order of the corresponding scaling function and the number of vanishing moments of the wavelets on .
Theorem 3.1.
[Theorem 7.4 in Chapter 7 of [9]] Let and be a wavelet and a scaling function that generate an orthogonal basis. Let be the refinement mask for the scaling function . Suppose that and . Then the following statements are equivalent:
-
1.
The wavelet has vanishing moments.
-
2.
and its first derivatives are zero at .
-
3.
and its first derivatives are zero at .
-
4.
For any ,
(3.1)
The hypothesis is called the Strang-Fix condition on .
Let be a dilation matrix on and denote . Let be a complete set of representatives of the distinct cosets of the quotient group . It is obvious that the cardinality of is equal to . Without loss of generality we assume that .
For any positive integer , a compactly supported function has vanishing moments if
where denotes the set of all polynomials of degree less than .
Let denotes the space of all finitely supported sequences on . Then a sequence has discrete vanishing moments if it satisfies the following equalities
where .
Proposition 3.1.
[10] Given a multiresolution analsysis on , with wavelets . Then , have vanishing moments if and only if the corresponding wavelet filters , have discrete vanishing moments.
The following proposition gives the relationship between the order of sum rules and the number of discrete vanishing moments on .
Proposition 3.2.
[10] Let satisfies the sum rules of order and . If satisfies , for any , then has discrete vanishing moments.
4 -Vanishing Moments on
This section gives a description of vanishing moments and discrete vanishing moments on .
Definition 4.1.
For any positive integer , a compactly supported function in is said to have -vanishing moments if
| (4.1) |
Theorem 4.1.
Let be a compactly supported function in . Suppose that is times pseudo-differentiable. Then has -vanishing moments if and only if .
Proof.
From the definition of the pseudo-differential operator we have,
Thus we have,
Then,
Hence, has -vanishing moments if and only if . ∎
Definition 4.2.
A sequence has discrete -vanishing moments if it satisfies the equalities
| (4.2) |
Theorem 4.2.
Given a multiresolution analsysis on , with wavelets . Then , , have -vanishing moments if and only if the corresponding wavelet filters , have discrete -vanishing moments.
Proof.
Theorem 4.3.
Let be a scaling function that generates a MRA and . Then the corresponding refinement mask of is of the form with . Moreover, has accuracy 1.
Proof.
Suppose that the scaling function that generates an MRA is given by
Then by (2.10),
Now by theorem 2.2, and for all . Also, we have . Thus we have a system of equations , where
Since are the roots of unity, the matrix is unitary. Also, is symmetric. Hence . Then has a solution . That is,
Hence , for all .
Remark 4.1.
This scaling function is called the Haar scaling function.
Theorem 4.4.
Let be a Haar scaling function and be the corresponding wavelets on . Then has only -vanishing moment for each .
Proof.
Here , where are chosen so that the matrix in (2.13) is unitary. That is satisfies the condition
| (4.5) |
Since is the Haar scaling function, , for all . Thus from (4.5), we can conclude that
Suppose that have -vanishing moments for . Then . That is,
which is possible only when , for all .
Since the matrix in (2.13) is unitary, its rows are orthonormal. That is . This is a contradiction, since , for all .
Hence has only -vanishing moment for each . ∎
Remark 4.2.
-
1.
If be an orthogonal scaling function that generates a MRA and . Then, by theorem 2.2, doesn’t satisfies the sum rules and has no accuracy.
- 2.
Example 4.1.
Let . Set if is not divisible by , and . Then
Thus, the corresponding refinement equation is where , . That is
| (4.6) |
The corresponding wavelet functions are
and
That is
Here has no accuracy. But and have -vanishing moment.
5 -Vanishing Moments of Haar-type Wavelets
In contrast to the real case, the wavelet basis generated by the Haar MRA in the -adic case is not unique. In [4], the authors provides an explicit description about the family of wavelet functions generated by the Haar MRA in .
Theorem 5.1.
[4] Let be the indicator function of and
Then the set of all compactly supported wavelet functions on are given by
| (5.1) |
where , and
| (5.2) |
and are entries of a unitary matrix .
Example 5.1.
Then we have . That is has -vanishing moment. Also,
Thus
That is has infinite number of -vanishing moments if and only if .
Example 5.2.
Let and . Then by (5.1), the wavelet function is given by
where
That is,
Then we have . That is has -vanishing moment. Also,
Thus
That is has infinite number of -vanishing moments if and only if .
Remark 5.1.
The following theorem gives a characterization for -vanishing moments of Haar-type wavelets.
Theorem 5.2.
Let be the wavelet finctions described in theorem 5.1. then always have -vanishing moment. Moreover, has infinite number of -vanishing moments if and only if .
Proof.
We have
where
for . Then
for . That is always have -vanishing moment.
We can write any , for in the form
Let for be the set given by,
and
Then
Thus, has infinite number of -vanishing moments if and only if . ∎
6 -Vanishing Moments of non-Haar type Wavelets
In [6], wavelet bases different from those described above were constructed; these bases were called non-Haar bases.
Theorem 6.1.
Theorem 6.2.
The following two theorems gives the connection between -vanishing moment of non-Haar type wavelet functions and the approximation order of the indicator function of the compact open subgroup of .
Theorem 6.3.
Let and be defined by (6.3). If has approximation order , then has -vanishing moments.
Proof.
We have for a fixed integer ,
That is, . Now,
Hence .
From theorem 2.4, has approximation order if and only if for all . Here .
We have has -vanishing moments if and only if for all . That is, if and only if for all .
Since is fixed, we can conclude that if has approximation order , then has -vanishing moments. ∎
Theorem 6.4.
Let and be the wavelet functions defined by (6.4). If has approximation order , then has -vanishing moments.
Proof.
For any fixed , let be the wavelet functions defined by (6.4),
with
where . That is,
Now,
Let and . Then applying the Leibniz formula for differentiation,
That is,
From theorem 2.4, has approximation order if and only if for all . Here .
We have has -vanishing moments if and only if for all . That is, if and only if for all .
Since is fixed, we can conclude that if has approximation order , then has -vanishing moments. ∎
Remark 6.1.
Let and be a non-Haar type wavelet function. If has approximation order , then has -vanishing moments.
7 -vanishing moments of nonorthogonal wavelets
In [15], the authors developed a method for constructing MRA-based p-adic wavelet systems that form Riesz bases in . The explicit construction is as follows:
For an integer , we set
It is easily seen that and for all . Moreover, and
Set . Let us define trigonometric polynomials and of degrees and , respectively, by
| (7.1) |
One can easily verify that for all .
Given integers and , we define the Fourier transform of functions and by
| (7.2) |
| (7.3) |
where .
Lemma 7.1.
[15] For all and , the following statements hold:
-
1.
and ,
-
2.
if and only if ,
-
3.
if and only if .
Set
| (7.4) |
Theorem 7.1.
It is easy to see that if or , then and we obtain the Haar MRA. On the contrary, for and , the functions generate pairwise distinct MRAs and each of these scaling functions is not orthogonal, which leads to nonorthogonal wavelet Riesz bases.
Example 7.1.
The following theorem characterizes the -vanishing moment of nonorthogonal MRA wavelets.
Theorem 7.2.
Let be the wavelet functions defined by (7.4). Then has -vanishing moments if and only if .
Proof.
We have . Then the Fourier transform of is given by
Then applying the Leibniz formula for differentiation,
Also . By the Leibniz formula for differentiation,
By Lemma 7.1 we have, . Thus we can see that has -vanishing moments if and only if . ∎
8 Conclusion
The definitions of -vanishing moments of compactly supported functions on and discrete -vanishing moments of finitely supported sequences on are given in this work. The relationship between the -vanishing moments and discrete -vanishing moments are established. The -vanishing moments of Haar-type and non-Haar type wavelet functions are calculated. We proved the connection between -vanishing moment of non-Haar type wavelet functions and the approximation order of the indicator function of the compact open subgroup of . Finally, we characterized the -vanishing moments of nonorthogonal wavelets.
Acknowledgement
We are very grateful to the authors of the articles in the references.
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