 Research Article
 Open Access
A New Scheme for the Design of Hilbert Transform Pairs of Biorthogonal Wavelet Bases
 Hongli Shi^{1}Email author and
 Shuqian Luo^{1}
https://doi.org/10.1155/2010/712105
© Hongli Shi and Shuqian Luo. 2010
 Received: 29 August 2010
 Accepted: 10 November 2010
 Published: 25 November 2010
Abstract
In designing the Hilbert transform pairs of biorthogonal wavelet bases, it has been shown that the requirements of the equalmagnitude responses and the halfsample phase offset on the lowpass filters are the necessary and sufficient condition. In this paper, the relationship between the phase offset and the vanishing moment difference of biorthogonal scaling filters is derived, which implies a simple way to choose the vanishing moments so that the phase response requirement can be satisfied structurally. The magnitude response requirement is approximately achieved by a constrained optimization procedure, where the objective function and constraints are all expressed in terms of the auxiliary filters of scaling filters rather than the scaling filters directly. Generally, the calculation burden in the design implementation will be less than that of the current schemes. The integral of magnitude response difference between the primal and dual scaling filters has been chosen as the objective function, which expresses the magnitude response requirements in the whole frequency range. Two design examples illustrate that the biorthogonal wavelet bases designed by the proposed scheme are very close to Hilbert transform pairs.
Keywords
 Discrete Wavelet Transform
 Filter Bank
 Wavelet Base
 Magnitude Response
 Optimization Routine
1. Introduction
The dualtree complex wavelet transform (DTCWT) had become an attractive signal processing tool since it was proposed by Kingsbury [1, 2]. It overcomes the main drawbacks of real discrete wavelet transform (DWT), such as shift sensitivity and poor directional selectivity (in the case of multidimensional DWT). A pair of filter banks is used in DTCWT, with which the wavelet bases associated form (approximate) Hilbert transform pairs. This property is critical since it is vital to reduce shift sensitivity and improve directionality [3].
Selesnick [4], and Ozkaramanli and Yu [5, 6] had studied the conditions under which the corresponding orthogonal wavelet bases form Hilbert transform pairs. It shows that if and only if one orthogonal lowpass filter is halfsample delayed from the other, the corresponding wavelets form a Hilbert transform pair. The compactly supported real orthogonal wavelets (except Haar wavelet) cannot be of linear phase (symmetry), however, linear phase is essential in many signal processing applications. As a result, the biorthogonal wavelets are usually suggested to be used, for example, JPEG2000 standard. Yu and Ozkaramanli [7] had further proven that the requirements of halfsample offset are also the necessary and sufficient condition for the design of Hilbert transform pairs of biorthogonal wavelet bases.
In [8], Selesnick proposed a new design scheme and designed two filter banks that the corresponding wavelets forming approximate Hilbert transform pairs. In [9], the design is formulated as a sampleddata optimization problem (suppose the primal filters are known) and then solved by standard approximation theory. The design scheme usually gives infinite impulse response (IIR) filters, which have to be truncated to form FIR filters. As a result, the dual filter bank is not symmetric and of perfect reconstruction (PR). In [7], the design is achieved by a constrained optimization procedure, which can ensure the filter bank to be symmetrical and PR. In the design, the objective function and constraints are all expressed in terms of the lowpass filters, that is, , , , and (see Section 2 for the notations). The objective function in [7] is equivalent to a measure, which involves a constrained minimax procedure. A series of objective functions are formed by selecting certain frequencies in , which express the magnitude response requirements at these discrete frequencies.
In this paper, a new design scheme is presented and the design implementation is described in detail. We have derived a relationship between the phase offset and the vanishing moment difference of two biorthogonal lowpass filters, which can be used to choose the vanishing moments so that the phase requirement is satisfied structurally. The design is also achieved by a constrained optimization procedure, in which the objective function and constraints are all expressed in terms of the auxiliary filters ( , , , and ) rather than the lowpass filters directly. When the lowpass filters are of high vanishing moments in total, the variable number can be reduced. The objective function chosen is equivalent to a measure, which expresses the magnitude response requirements in the whole frequency range. Therefore, a better approximation quality to Hilbert transform pair is possible.
2. Preliminaries
2.1. Biorthogonal Wavelet
Equation (4) is equivalent to (1) or (2), which is the foundation in designing biorthogonal wavelet.
2.2. Hilbert Transform Pairs of Biorthogonal Wavelets
(In the design example, Antonini filters are chosen as and , and the constraintis are no longer necessary).
The optimization routine (9) involves variables, where denotes the greatest integer not exceeding and , , , and denote the lengths of , , , and , respectively. The design scheme (9) involves a constrained minimax routine. In the design, a series of objective functions are formed by choosing certain frequencies, which express the magnitude response requirements at discrete frequencies. Since the lowpass filters can be represented by their auxiliary filters uniquely, the optimization routine can be expressed by the auxiliary filters uniquely. If the filters have high vanishing moments in total, the variable number of objective function and constraints can be reduced. As a result, the calculation burden in optimization routine can be reduced. Furthermore, the objective function can be chosen to express the magnitude response requirement in whole frequency range.
3. A New Design Scheme
The new design scheme is also based on the sufficient and necessary conditions that give Hilbert transform pairs of biorthogonal wavelet base, that is, (8). First of all, the biorthogonal filter banks should be designed to satisfy PR requirement.
where ; is an odd polynomial. Since is symmetric and has taps, we can derive constraints on the filter and and the polynomial coefficients of according to (10). Similarly, we can derive constraints on and and from (11).
Equations (10) and (11) ensure that the filters and , , and satisfy PR requirement, respectively. If , that is, the polynomial , is not unique, and so are and . Similarly, if , and are not unique. Therefore, it is possible to construct biorthogonal filter banks that satisfy the PR requirement in addition to approximately satisfying (8) when and/or .
For the requirement of halfsample offset, first consider the requirement of phase group delay/advance. The relationship between the phase offset and the vanishing moment difference of two biorthogonal lowpass filters can be characterized by the following proposition.
Proposition 1.
where denotes the phase angle; .
Proof
Therefore, .
In [1, 2], Kingsbury had chosen the odd/evenlength filter pairs in designing the Hilbert transform pairs wavelet bases. This proposition shows that the choice is unique.
In this way, the objective function and constraints are all expressed by the auxiliary filters. The constraints in (16) are equivalent to these in (9) essentially. If the lowpass filters have high vanishing moments in total, the variable number of objective function and constraints in (17) is less than that in (9). For example, the variables in (17) corresponding to the primal filters consist of the coefficients of and , whose numbers are and , respectively. The total number in (9) is . Therefor, if , the variable number in (17) is less than that in(9). In Example 1, and are all of taps with , and and are, respectively, tap and of taps with . The variable number for the design scheme (17) is 20 however, it is 25 for the design scheme (9). Therefore, the calculation burden in optimization process can be reduced efficiently because of less variables. In fact, the constraints in (9) can be expressed by the auxiliary filters only (without the coefficients of and ) therefore, the variable number can be further reduced, and only the expressions become quite complicated.
4. Design Examples
Two biorthogonal filter banks are designed, whose corresponding wavelet bases , , , and form Hilbert transform pairs, that is, they satisfy (6).
Example 1.
In this example, the design process can be described by the following steps.
(1) Selection Vanishing Moments
suppose the vanishing moments of the primal and dual filter banks are , , , and , respectively. and ( and ) are either both odd or even; it means that , and . According to the proposition in the previous section, it is required that . In order to make the two filters have equivalent vanishing moments, we select , . We select , to make the two filter banks have the equivalent lengths and , .
(2) Forming Constraints
Similarly, the constraints on and can be formed.
The filter coefficients in Example 1.









−5,6  −0.003744218100161  −5,6  0.001973944277540  −6,6  −0.002679477007908  −5,5  0.006291060002458 
−4,5  0.027155595407045  −4,5  0.014316375468255  −5,5  0.007973992387869  −4,4  0.018721886555915 
−3,4  0.001335375233427  −3,4  0.015452878287554  −4,4  0.036852253064238  −3,3  0.006861425697625 
−2,3  −0.207583964407648  −2,3  0.007742782192903  −3,3  −0.097728862384778  −2,2  0.048456665186341 
−1,2  −0.036015217586016  −1,2  0.143297943721683  −2,2  −0.216559663541172  −1,1  0.274347514299916 
0,1  0.653635038149005  0,1  0.3922160760 52064  −1,1  0.307146174344736  0  0.440642896515488 
0  0.799556383665335 
Example 2.
The coefficients of lowpass filters in Example 2
The filter lengths are of with  





−4,5  0.005177090817695  −5,4  0.003154113122922 
−3,4  0.021637688714158  −4,3  −0.013182638730184 
−2,3  −0.062344308626311  −3,2  −0.064516826255491 
−1,2  0.030709855881950  −2,1  0.092188868846268 
0,1  0.508721114417255  −1,0  0.478485248598678 
5. Conclusion
This paper describes a new scheme for the design of Hilbert transform pairs of biorthogonal wavelets in detail. The relationship between the phase offset and the difference of vanishing moments is useful in choosing the vanishing moments so that the phase requirement on the corresponding lowpass filters can be satisfied strictly. The design is also simplified as a constrained optimization procedure. The objective function and constraints are all expressed in terms of the auxiliary filters, which can reduce the total variable number when the lowpass filters are of high vanishing moments. Consequently, the computational complexity can be reduced efficiently. The objective function expresses the magnitude response requirements in the whole frequency range properly, which is useful in improving the approximation quality to Hilbert transform pairs.
Declarations
Acknowledgments
This work has been supported by National Natural Science Foundation of China (NSFC) under Grant no. 60972156 and Beijing Natural Science Foundation under Grant no. 4102017.
Authors’ Affiliations
References
 Kingsbury NG: Image processing with complex wavelets. Philosophical Transactions of the Royal Society A 1999, 357(1760):25432560. 10.1098/rsta.1999.0447View ArticleMATHGoogle Scholar
 Kingsbury NG: Complex wavelets for shift invariant analysis and filtering of signals. Applied and Computational Harmonic Analysis 2001, 10(3):234253. 10.1006/acha.2000.0343MathSciNetView ArticleMATHGoogle Scholar
 Fernandes FCA, Selesnick IW, Van Spaendonck RLC, Burrus CS: Complex wavelet transforms with allpass filters. Signal Processing 2003, 83(8):16891706. 10.1016/S01651684(03)00077XView ArticleMATHGoogle Scholar
 Selesnick IW: Hilbert transform pairs of wavelet bases. IEEE Signal Processing Letters 2001, 8(6):170173. 10.1109/97.923042View ArticleGoogle Scholar
 Ozkaramanli H, Yu R: On the phase condition and its solution for Hilbert transform pairs of wavelet bases. IEEE Transactions on Signal Processing 2003, 51(12):32933294. 10.1109/TSP.2003.818996MathSciNetView ArticleGoogle Scholar
 Yu R, Ozkaramanli H: Hilbert transform pairs of orthogonal wavelet bases: necessary and sufficient conditions. IEEE Transactions on Signal Processing 2005, 53(12):47234725.MathSciNetView ArticleGoogle Scholar
 Yu R, Ozkaramanli H: Hilbert transform pairs of biorthogonal wavelet bases. IEEE Transactions on Signal Processing 2006, 54(6):21192125.View ArticleGoogle Scholar
 Selesnick IW: The design of approximate Hilbert transform pairs of wavelet bases. IEEE Transactions on Signal Processing 2002, 50(5):11441152. 10.1109/78.995070MathSciNetView ArticleGoogle Scholar
 Yu R: Characterization and sampleddata design of dualtree filter banks for Hilbert transform pairs of wavelet bases. IEEE Transactions on Signal Processing 2007, 55(6):24582471.MathSciNetView ArticleGoogle Scholar
 Daubechies I: Ten Lectures on Wavelets. SIAM, Philadelphia, Pa, USA; 1992.View ArticleMATHGoogle Scholar
 Walnut D: An Introduction to Wavelet Analysis. Birkhäuser, Boston, Mass, USA; 2002.MATHGoogle Scholar
 Cohen A, Daubechies I, Feauveau JC: Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics 1992, 45: 485560. 10.1002/cpa.3160450502MathSciNetView ArticleMATHGoogle Scholar
 Tay DBH, Kingsbury NG, Palaniswami M: Orthonormal Hilbertpair of wavelets with (almost) maximum vanishing moments. IEEE Signal Processing Letters 2006, 13(9):533536.View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.