Abstract
The deformed wavelet transform is a new addition to the class of wavelet transforms that heavily rely on a pair of generalized translation and dilation operators governed by the well-known Dunkl transform. In this study, we adapt the symmetrical properties of the Dunkl Laplacian operator to prove a class of quantitative uncertainty principles associated with the deformed wavelet transform, including Heisenberg’s uncertainty principle, the Benedick–Amrein–Berthier uncertainty principle, and the logarithmic uncertainty inequalities. Moreover, using the symmetry between a square integrable function and its Dunkl transform, we establish certain local-type uncertainty principles involving the mean dispersion theorems for the deformed wavelet transform.
MSC:
Primary 44A05; Secondary 42A68; 42B10; 42C20
1. Introduction
A finite subset R of is called a root system if , and for all , we have , where is the reflection in the hyperplane which is orthogonal to .
Let , be the Dunkl operators [], related with a root system R and a multiplicity function k that is invariant under the action of the reflection group associated to R.
The Dunkl transform (see []) is a weighted integral transform with kernel , that is,
where is a weight measure, and for all , the Dunkl kernel satisfies the following system
This transformation extends the Fourier transform (if ), given by
and realizes an isometry from onto itself, that is, for all ,
For more details about Dunkl’s theory, authors can refer to [,,].
Thanks to the importance of time-frequency analysis, the theory of uncertainty principle has attracted a considerable attention and it has been extended to a large class of integral transformations (see, e.g., [,,,,] and the references therein). The famous Heisenberg’s uncertainty principle asserts that a non-trivial function cannot be precisely concentrated at the same time in the time and frequency domains, and in this case the concentration (or localization) is measured by means of dispersions, that is,
where is the Euclidean norm on , and its analog in the Dunkl setting (see []) is,
Several extensions of Heisenberg’s uncertainty principle appear in the literature, for example, the Beckner-type logarithmic uncertainty principles [,], Benedicks’ uncertainty principle [], the entropy uncertainty principles, the local uncertainty inequalities, and many others (see, e.g., [,]). These inequalities are stronger than Heisenberg’s uncertainty principle, in the sense that from each of these extensions we can derive a Heisenberg-type uncertainty inequality.
In this paper, we are interested in finding some uncertainty principles for the new transformation called the deformed wavelet transform defined by
where h is a deformed wavelet, is the Dunkl translation operator, and is a dilation operator (see Section 2.3 for details about the deformed wavelet transform). The first of our results is the Heisenberg-type inequality. We will prove this principle via different techniques (see Section 3 for details). In particular, we have the following inequality: For all ,
where the constant does not depend on f and . Then, we will prove a Beckner-type and logarithmic uncertainty inequalities. The proofs of these principles are based on the well-known Pitt’s and Sobolev’s inequalities (see Section 4 for details). Finally, we will show some uncertainty inequalities, in which the localization is measured by means of smallness of supports, such as Faris’s local uncertainty inequalities and the Benedicks’ uncertainty principle (see Section 5 for details).
This article is structured as follows: In Section 2, we recall some preliminary results about the Dunkl and deformed wavelet transforms. Section 3 is devoted to proving some new Heisenberg-type uncertainty inequalities for the deformed wavelet transform. In Section 4, we will show Pitt-type, Sobolev-type, and Beckner-type inequalities. Finally, in Section 5, we obtain some concentration uncertainty principles for sets of finite measures.
2. Preliminaries
In this section, we give some preliminary results about the Dunkl theory. The main references are [,,,,,,].
2.1. The Dunkl Operators
Let be the orthonormal basis of and be the Euclidean norm of , where is the usual scalar product. If , then we define as the reflection in the hyperplane that is orthogonal to ,
Let R be a finite subset of . Then, R is called a root system if and for all , we have .
The reflections , generate a finite group W of , which is called the reflection group associated with R.
Let . Then, we fix the positive root system , and we assume that for all : .
A multiplicity function is a function that is invariant under the action of W. Then, we define the index by
Moreover, we define the weight function by
In particular, is homogeneous to degree and W-invariant.
If , then the weight function is defined by
It is well known that the Mehta-type constant is defined by
Now, we shall fix some notation. Let
- (1)
- be the ball of ,
- (2)
- },
- (3)
- be the subspace of with compact supports,
- (4)
- be the Schwartz space, and its topological dual.
The Dunkl operators on associated with W and k are defined for a given by
where is the space of -functions on , .
The Dunkl–Laplacian operator on is defined for a given -function f, by
where Δ and ∇ are, respectively, the usual Euclidean Laplacian and the gradient operators on .
Let . Then the Dunkl kernel is the unique analytic solution of the following system:
This kernel admits a unique holomorphic extension to and satisfies the following well-known relations:
- (1)
- For all and all ,
- (2)
- For all , and ,whereIn particular, for all :
- (3)
- For all and , the kernel K has the following Laplace-type integral representation:where is a positive probability measure on , with support in the ball (see []).
2.2. The Dunkl Transform
Let be the space of measurable functions on such that
where
In particular, is the Hilbert space equipped with the scalar product
Let . Then, we set
the subspace of radial functions in . Notice that if , then there exists a unique complex function G defined on such that , for all .
Remark 1.
Since is homogeneous, for any radial function g in , the function G defined on by is integrable with respect to the measure , and satisfies (see []),
where
The Dunkl transform of an integrable function is defined by
and it satisfies the following properties (see [,]):
- (1)
- For any ,
- (2)
- If , then we have the following inversion formula:
- (3)
- The Dunkl transform is an isomorphism from onto itself, such that
- (4)
- For any , the following Plancherel’s formula holds:
- (5)
- The Dunkl transform can be uniquely extended to an isometric isomorphism on .
- (6)
- For all , the following Parseval’s formula holds:
- (7)
- For all (resp. ,andwhere .
Now, we will define The Dunkl translation operator (see []).
Definition 1.
For , the Dunkl translation operator is defined on by
The Dunkl translation operator satisfies the following properties (see [,]).
Proposition 1.
Let and let , the generalized Wigner space (see []). Then,
- (1)
- For any function ,
- (2)
- For any function ,
- (3)
- For any function ,
The explicit formula of the Dunkl translation operator is still an open question, and it is known only in some special cases. In particular, if and , then for all and ,
where
From this, one can also give an explicit formula for the translation operator in the case of . Moreover we have the following boundedness result (see [,]).
Proposition 2.
Let and let . Then, for any function ,
On the other hand, if f is a radial function in , then
where F is the function defined by .
Let be the subspace of radial functions in . Then, we have the following results (see []).
Proposition 3.
Let and .
- (1)
- If is nonnegative, thenand
- (2)
- If , then
The Dunkl translation operator is an essential tool to define the Dunkl convolution product (see [,]).
Definition 2.
Let . Then, the Dunkl convolution product is defined by:
The Dunkl convolution operator is associative and commutative and verifies the following well-known relations (see [,]).
Proposition 4.
Let such that
- (1)
- If and , then such that,
- (2)
- If , then for all and , the function belongs to such that
- (3)
- If , then if and only if , and in this case,
- (4)
- If , then
For the remainder of this article, we will assume that .
2.3. Deformed Wavelet Transform
From the reference [], we will recall some preliminaries results about the deformed wavelet transform and some of its properties. First, we will fix some notation.
The dilation operator , , of a measurable function f is defined by
This operator satisfies the following relations.
Proposition 5.
Let .
- (1)
- We have
- (2)
- If , then such thatand
- (3)
- If , , then such that
- (4)
- If , then
- (5)
- For all ,
Definition 3.
A deformed wavelet on is a measurable function h on satisfying the following condition: For almost all ,
Proposition 6.
If h is a deformed wavelet on , then
Example 1.
Let . Then, the function defined by
satisfies
Thus, the function is a deformed wavelet on that belongs to .
For and h, a deformed wavelet in , we define the family , , of functions in by
Then, for all and ,
For , we denote by the space of measurable functions f on such that
where is the weight measure defined by
Definition 4.
Let be a deformed wavelet. Then, the deformed continuous wavelet transform is defined for a regular function f by
or equivalently
Remark 2.
Let be a deformed wavelet. Then,
- (1)
- For all ,
- (2)
- For all and all ,where
Lemma 1.
Let be a deformed wavelet. Then, for all ,
Theorem 1 (Parseval-type formula for ).
Let be a deformed wavelet. Then, for all ,
Corollary 1 (Plancherel-type formula for ).
Let be a deformed wavelet. Then, for all ,
Then, the Riesz–Thorin interpolation theorem implies this result.
Proposition 7.
Let be a deformed wavelet and . Then, for all ,
Theorem 2 (An inversion formula for ).
Let be a deformed wavelet. Then for all f in (resp. ) such that (resp. ),
Proof.
Using similar ideas as in the proof for Theorem 6.III.3 of [] p. 99, we obtain the relation (58). □
3. Heisenberg-Type Uncertainty Principles for the Deformed Wavelet Transform
In this section, we prove several versions of the Heisenberg-type uncertainty inequalities for the deformed wavelet transform via different techniques, including the generalized entropy, the contraction semigroup method of the homogeneous integral transform, and others.
3.1. Heisenberg-Type Uncertainty Inequalities for Functions in
First, we recall the following Heisenberg-type uncertainty inequality for the Dunkl transform, first proved by Rösler [] and generalized in [].
Proposition 8.
For all , there exists a constant , such that for all , the following holds:
For , .
Our first result is the following Heisenberg-type uncertainty inequality for .
Theorem 3.
Let . Then, for all we have
where is the same constant given in Proposition 8.
Proof.
By (44), we have
Involving (54), we obtain
Integrating both sides with respect to the measure , we obtain, by Hölder’s inequality and Plancherel’s formula,
This proves the result. □
Now, we will prove another version of Heisenberg’s uncertainty principle for the deformed wavelet transform. To do so, we have first to prove prove an uncertainty inequality involving entropy. Let be a probability density function on i.e.,
Following [], the k-entropy of is defined by
Then, we have the following entropy uncertainty inequality for the deformed wavelet transform.
Proposition 9.
For all we have
Proof.
Assume that By (52),
Particularly Next, let
Then, and
Therefore, Moreover,
which implies
Using the fact that we deduce that
This completes the proof. □
From the last result, we can derive a new version of Heisenberg’s uncertainty inequality for .
Theorem 4.
For all , there exists a constant such that for all , we have
where
Proof.
Let , be the function defined on by
By simple computation, we see that
It is clear that is a probability measure on . Then, by Jensen’s inequality we obtain
Therefore,
If , then, by Proposition 9, we obtain
However, the expression
attains its upper bound at
, and consequently,
where
Therefore, for every and such that we obtain
Now, replacing f by in the last inequality, we obtain,
Using (53), we deduce that
In particular, the inequality holds at the point
which implies that
where
Now, the desired result follows from above, by substituting f by and h by □
Remark 3.
If , then
3.2. –Heisenberg’s Uncertainty Principles for the Deformed Wavelet Transform
Let . We put
By simple calculations, it is easy to prove the following.
Lemma 2.
Let and . There exists a positive constant C such that we have
Lemma 3.
We assume that . Let and . Then, there exists a positive constant C such that, for all and ,
Proof.
Inequality (64) holds if Assume that
On the other hand, by (52) and Hölder’s inequality
A simple computation gives:
So
Choosing , we obtain (64). □
Theorem 5.
Let and and . Then, there exists a positive constant C such that, for all ,
Proof.
Let and . Assume that . From the previous lemma, for all ,
On the other hand,
Since is bounded for if , we obtain
from which, optimizing in , we obtain (65) for and .
If , let . For , we have , which for gives the inequality
It follows that
Optimizing in , we obtain
Together with (65) for , we obtain the result for . □
Corollary 2.
Let and . Then, there exists a positive constant C such that, for all , we have
Proof.
Using the previous theorem for , and applying Plancherel’s Formula (56), we obtain the result. □
4. Weighted Inequalities for the Deformed Wavelet Transform
In this section, our motive is to formulate some weighted uncertainty inequalities for the deformed wavelet transform. To accomplish this motive, firstly, we begin our study with the following.
4.1. Pitt’s Uncertainty Principle
The Pitt inequality in the Dunkl setting expresses a fundamental relationship between a sufficiently smooth function and the corresponding Dunkl transform. This subject was first studied in []; then, Gorbachev et al. in [] improved it and provided in the Dunkl setting a sharp Pitt-type inequality and a logarithmic uncertainty inequality. Specifically, for all f in the Schwartz space and ,
where
and denotes the well known Euler’s Gamma function.
The first main objective of this Subsection is to formulate an analogue of Pitt’s inequality (67) for the deformed wavelet transform.
Theorem 6.
For any arbitrary , the Pitt-type inequality for the deformed wavelet transform is given by
Proof.
Invoking Lemma 1, we can express the inequality (71) in the following manner:
Equivalently, we have
Thus
which proves the desired result. □
In the following theorem, we derive the Beckner’s inequality for the deformed wavelet transform by using the logarithmic estimate obtained from (67).
Theorem 7.
For any function , the following inequality holds:
4.2. Beckner-Type uncertainty principle
The Beckner-type inequality (see []) for the Dunkl transform states that for all ,
This inequality is also known in the literature as the logarithmic uncertainty inequality.
Now, we will give an other proof of Theorem 7 by using the Beckner-type inequality (83).
Proof of Theorem 7.
Integrating (84) with respect to the measure ,
Using Plancherel’s formula (56), we obtain
We shall now simplify the second integral of (86). By using Lemma 1, we infer that
The proof is complete. □
Corollary 3.
Let be a deformed wavelet such that . Then, for all ,
Proof.
From (75) and Jensen’s inequality, we obtain
which gives the desired result. □
Remark 4.
- (1)
- From the approximationwe obtainwhich is the same constant given in Theorem 3.
- (2)
- Proceeding as above in logarithmic uncertainty inequality (83) we deduce the following Heisenberg uncertainty inequality:
- (3)
4.3. Dunkl Logarithmic Sobolev Inequalities
In this subsection, we will prove some logarithmic Sobolev-type uncertainty principle for the deformed wavelet transform. First, we will recall some preliminary results.
Definition 5.
Let be a distribution. Then
- (1)
- Its The Dunkl transform is defined by
- (2)
- For all ,
Definition 6.
The Dunkl Sobolev space of order is defined by
Definition 7.
Let and . Then, we define the following weighted Lebesgue space by
where for , we have .
We recall in the following the logarithmic uncertainty inequality in the Dunkl setting.
Theorem 8.
There exists a positive constant such that for all ,
where
In the next theorem, we give another version of uncertainty inequality for the deformed wavelet transform.
Theorem 9.
Let be a deformed wavelet. Then, for all ,
Proof.
From (96),
Integration the last inequality with respect to the measure , we obtain
By (97) and Lemma 1, we have
The proof is complete. □
From Inequality (9), we can derive the following new uncertainty principle for the deformed wavelet transform.
Theorem 10.
Let be a deformed wavelet with . Then, for all ,
5. Concentration Uncertainty Principles for the Deformed Wavelet Transform
In this section, we shall derive some concentration-based uncertainty principles for the deformed wavelet transform such as the Benedick–Amrein–Berthier and local-type uncertainty principles.
5.1. Benedick–Amrein–Berthier’s Uncertainty Principle
In [], the authors proved a Benedicks-type uncertainty principle in the Dunkl setting, that is, if are of finite measures, then there exists a constant (called the annihilating constant), such that for all
In the next theorem, we state the analog of (105) for the deformed wavelet transforms.
Theorem 11.
For all ,
Proof.
By integrating (107) with respect to measure ,
Using Lemma 1 together with Plancherel’s Formula (56), the above inequality becomes
which further implies
Since h is a deformed wavelet, we have
which is the desired Benedicks–Amrein–Berthier’s uncertainty principle for the deformed wavelet transform. □
From Theorem 11, we derive the following general uncertainty inequality.
Corollary 4.
For all , there exists a constant such that for all ,
5.2. Local-Type Uncertainty Principle
The classical Heisenberg uncertainty principle states that if a signal f is well concentrated in the natural domain, the corresponding Fourier transform cannot be properly localised at a point in the spectral domain. However, it does not preclude f from being localised within a small neighbourhood of two or more widely separated points. In fact, the latter phenomenon cannot occur either, and it is the motive of local uncertainty inequalities to make this precise. In this subsection, our goal is to derive some local uncertainty principles for the deformed wavelet transform. We begin this subsection by recalling the local uncertainty principle for the Dunkl transform [].
Proposition 10.
Let E be a subset of with finite measure . For , there exists a constant such that for all
We are now ready to obtain the local uncertainty principle for the deformed wavelet transform by employing the inequality (108).
Theorem 12.
Let with finite measure and let . Then, for all ,
Proof.
Now, integrating the last inequality with respect to the measure ,
which, together with Lemma 1 and Fubini’s theorem, give
The proof is complete. □
Corollary 5.
Let and let with finite measure. Then, for all ,
where is the Paley–Wiener space.
By interchanging the roles of f and in Proposition 10, we obtain the following:
Corollary 6.
Let with finite measure and let . Then, for all , we have
Using Corollary 6 and by adapting the proof of Theorem 12, we obtain this result.
Corollary 7.
Let with finite measure and let . Then for all ,
Let F be a subset of . We define the generalized Paley–Wiener space :
Then, by Formula (56), and the previous corollary, we obtain the following result.
Corollary 8.
Let E and F be two subsets of such that , and let . Then,
- (1)
- For any ,
- (2)
- For any ,
From Theorem 12, we derive the following result.
Theorem 13.
Let and . Then for all ,
where
Proof.
Let and . Then,
From Theorem 12 and by simple calculation, we have
Moreover it is easy to see that
Combining the relations (131)–(133), we obtain
We choose
and we obtain the desired inequality. □
5.3. -Local Type Inequality
The aim of this subsection is to derive an -local type inequality for the deformed wavelet transform. The following theorem gives the main result.
Theorem 14.
Let T be any measurable subset in with . Then, for every and , we have
where
Proof.
(i) For , we consider and . Using (57) and the fact that , we obtain
On the other hand, relation (52) and Hölder’s inequality imply that
Moreover, a simple calculation yields
Consequently, we have
With the choice
we obtain the first inequality.
(ii) Assume that Again applying Hölder’s inequality and (52), we obtain
Using Hölder’s inequality together with the fact that , we obtain
Upon replacing by , in the above inequality, we obtain
In particular, the inequality also holds at
which implies that
with
(iii) Using the inequality
we obtain
By optimizing over r, we obtain
Similarly, we can prove that
Thus, we conclude that
This completes the proof of Theorem 14. □
Remark 6.
We note that when , we can obtain a family of inequalities and improve the inequality given in Theorem 14. Indeed, if we apply the first inequality with
and then apply the classical inequality
we obtain, for all ,
Definition 8.
Let , and .
- (1)
- A nonzero is called an ε-concentrated function on S in -norm if
- (2)
- A nonzero is called an ε-bandlimited function on T in -norm if
Here, is the complement of A.
Corollary 9.
Let such that .
- (1)
- If , then there exists a positive constant C such that for every function f that is ε-bandlimited on T,
- (2)
- If , then there exists a positive constant C such that for every function f that is ε-bandlimited on T,
- (3)
- For all , there exists a positive constant C such that for every function f that is ε-bandlimited on T,
Proof.
Since is -bandlimited on T, it follows that
Corollary 10.
Let and . Then, for all , we have
where denotes the Lorentz space corresponding to the norm
and , for are the constants given in Theorem 14.
Theorem 15.
Let and . Then, for all , we have
where
and , for are the constants given in Theorem 14.
Proof.
(i) Let , and . Then,
From Theorem 14, we have
Moreover, it is easy to see that
Combining the relations (131)–(133), we obtain
We choose
to obtain the first inequality.
(ii) Let , and . From Theorem 14, we have
Combining the relations (131), (133), and (134), we obtain
We choose
to obtain the second inequality.
(iii) Let , and . From Theorem 14, we have
Combining the relations (131), (133), and (135), we obtain
We choose
where
to obtain the third inequality. □
Corollary 11.
Let . Then, for all , we have
We close this subsection with the following local uncertainty principle version:
Theorem 16 (Faris–Price’s uncertainty principle for ).
Let be two real numbers such that and . Then, there is a positive constant such that for all and any with ,
Proof.
Assume that . Then, for every ,
where However, by Hölder’s inequality and relation (52) we obtain, for every ,
On the other hand by simple calculations, we see that
Thus, we obtain
On the other hand, and again by Hölder’s inequality and Relation (52), we deduce that
Hence, for every ,
In particular, the inequality holds for
and therefore
Finally, the general result follows from the above inequality by replacing f with and h with . □
6. Conclusions
In this paper, we have established some new uncertainty principles for the deformed wavelet transform in which localization is measured by generalized dispersions such as in the Heisenberg-type uncertainty inequalities, and other principles in which localization is measured by smallness of the supports such as in Benedicks-type and local uncertainty inequalities. We have provided detailed proofs for these uncertainty principles. Future studies will focus on the explicit expression of this transformation in some special cases and then give some applications of the proposed transform in image processing.
Author Contributions
Conceptualization, S.G. and H.M.; methodology, S.G. and H.M.; Software, S.G. and H.M.; Validation, S.G. and H.M.; Formal analysis, S.G. and H.M.; Investigation, S.G. and H.M.; Resources, S.G. and H.M.; Data curation, S.G. and H.M.; Writing original draft preparation, H.M.; writing—review and editing, S.G.; Visualization, S.G. and H.M.; Supervision, S.G. and H.M.; Project administration, S.G.; Funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [GRANT No. 2843].
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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