# The biorthogonal wavelets that are redundant-free and nearly shift-insensitive

- Hongli Shi
^{1}Email author and - Shuqian Luo
^{1}

**2012**:14

https://doi.org/10.1186/1687-5281-2012-14

© Shi and Luo; licensee Springer. 2012

**Received: **20 June 2012

**Accepted: **23 August 2012

**Published: **7 September 2012

## Abstract

It is well known that discrete wavelet transform (DWT) is sensitive to shift, which means a slight shift of feature in the original signal may cause unpredictable changes in the analysis subbands. Some modified versions of DWT can reduce the shift sensitivity, however, they are all redundant. In this article, we shows the shift sensitivity is caused by the aliasing terms formed in the downsampling operation during analysis process. A novel scheme for the design of wavelet is proposed to reduce the effect of aliasing terms as much as possible in the general framework of DWT. A few of biorthogonal wavelets have been designed and applied in the simulation examples. The results of examples demonstrate the efficiency of the designed wavelets in the term of shift insensitivity and nonredundancy.

### Keywords

Biorthogonal wavelet DWT Shift sensitivity## Introduction

Discrete wavelet transform (DWT) has been applied in many fields as a tool of signal processing, e.g., signal de-noising, feature extraction, pattern recognition and image registration[1–3]. However, DWT is shift-sensitive[4]. A slight shift of feature in the original signals or images may generate unpredictable changes in its DWT analysis subbands. For example, for level-*d* low-low (LL) subbands of a two-dimensional figure, only the features that consist of more than 2^{2d} pixels in the original images can be insensitive to shift in image registration[5]. Some new wavelets and modified calculation frameworks of DWT have been presented to reduce the shift sensitivity. However, all these DWTs become redundant, i.e., they are no longer critically-sampled[6–10]. For example, Kingsbury’s dual tree complex wavelet transform (DTCWT) and Selesnick’s double-density wavelet transform (DDWT) are all redundant. A pair of filter banks is employed in Kingsbury’s DTCWT, which leads to a constant redundancy rate of 2:1 for 1-D signals and 2^{
m
}:1 for m-dimensional signals. The other modified DWTs, such as DDWT, also cannot be critically-sampled. The lack of directionality is other main drawback of DWTs. Many modified transformations have been presented to improve directional selectivity, such as curvelet and contourlet transformations[11–13]. These multiresolution representations are much more redundant.

## The shift sensitivity of DWT

*H*

_{0}and$\overline{{\stackrel{~}{H}}_{0}\left(z\right)}$ denote the analysis and synthesis filters in the low-pass branch.

*H*

_{0}and${\stackrel{~}{H}}_{0}\left(z\right)$ can be designed to be symmetrical to ensure linear-phase.$\overline{{\stackrel{~}{H}}_{0}\left(z\right)}$ denotes the complex conjugate of${\stackrel{~}{H}}_{0}\left(z\right)$.

*H*

_{1}and$\overline{{\stackrel{~}{H}}_{1}\left(z\right)}$ are the corresponding filters in the high-pass branch. Let

*X*(

*z*) denote the original signal. The analysis subbands become

*↓*2” denotes the downsampling operation by the factor of “2”. The synthesis signal

*Y*(

*z*) becomes

*↑*2” denotes the upsampling process by the factor of “2”. The no-aliasing condition and no-distortion condition require

**The filter bank (** Q **(** z **) and**$\stackrel{\mathbf{~}}{\mathbf{Q}}\mathbf{\left(}\mathbf{z}\mathbf{\right)}$ are **9**- and **3**-tap,$\mathbf{k}\mathbf{=}\mathbf{11},\stackrel{\mathbf{~}}{\mathbf{k}}\mathbf{=}\mathbf{1}$**)**

n | F | n | G |
---|---|---|---|

−1 2 | −1.620457579834970 | −2 2 | 0.183914612820184 |

0 1 | −14.856833358190139 | −1 1 | 6.521032167975675 |

0 | 20.173189134221989 | ||

n |
H
| n | ${\stackrel{\mathbf{~}}{\mathbf{H}}}_{\mathbf{0}}$ |

−10 9 | 0.000359208228164 | −1 2 | −0.334521746568079 |

−9 8 | 0.000786334299193 | 0 1 | 0.732293701891982 |

−8 7 | −0.002321674814646 | ||

−7 6 | −0.003720186105568 | ||

−6 5 | 0.017033423809928 | ||

−5 4 | 0.031465370920245 | ||

−4 3 | −0.020504827773706 | ||

−3 2 | 0.006960043873026 | ||

−2 1 | 0.373526016710723 | ||

−1 0 | 0.853417930379625 |

**The filter bank (**
Q
**(**
z
**) and**
$\stackrel{\mathbf{~}}{\mathbf{Q}}\mathbf{\left(}\mathbf{z}\mathbf{\right)}$
**are 19- and 3-tap,**
$\mathbf{k}\mathbf{=}\stackrel{\mathbf{~}}{\mathbf{k}}\mathbf{=}\mathbf{1}$
**)**

| F |
| G |
---|---|---|---|

−4 5 | 0.036875746448608 | −4 4 | 0.011220065157315 |

−3 4 | −0.023135612111302 | −3 3 | 0.033448583455217 |

−2 3 | 0.117860494348029 | −2 2 | 0.104853324526458 |

−1 2 | 0.087646010428631 | −1 1 | 0.302593640841847 |

0 1 | 0.710918453811750 | 0 | 0.927449000805610 |

n |
H
| n | ${\stackrel{\mathbf{~}}{\mathbf{H}}}_{\mathbf{0}}$ |

−10 9 | 0.036875746448607 | −2 1 | −0.445094807808344 |

−9 8 | 0.048095811605923 | −1 0 | 0.580522378115357 |

−8 7 | −0.011915546953987 | ||

−7 6 | 0.010312971343915 | ||

−6 5 | 0.151309077803246 | ||

−5 4 | 0.222713818874487 | ||

−4 3 | 0.192499334955090 | ||

−3 2 | 0.390239651270478 | ||

−2 1 | 1.013512094653598 | ||

−1 0 | 1.638367454617361 |

*X*

_{ l }(

*z*) and

*X*

_{ h }(

*z*), become shift-sensitive because of the aliasing terms. The shift sensitivity can be shown in many ways. Here, it is shown in the following way. Suppose

*X*(

*z*) is delayed by one sample and denoted as

*X*

^{ ′ }(

*z*), i.e.,

*X*

^{ ′ }(

*z*) =

*z*

^{−1}

*X*(

*z*), the low-pass output${X}_{l}^{\prime}\left(z\right)$ becomes

*x*(

*n*) in Figure2a consists of five pulses (the pulse width is 2-pixel) at different positions (the period is 101-pixels) with the same magnitudes. It equals to a pulse and its shifted versions. The low-pass output of first level DWT are shown in Figure2b using biorthogonal wavelet “bior3.9” (the analysis and synthesis filters are 20- and 4-tap, respectively). It shows analysis outputs of these pulses becomes quite different. Generally, the low-pass subbands are more insensitive to shift than the high-pass subbands. Even though for the low-pass subbands, the shift sensitivity is still unacceptable in some applications. With wavelet analysis proceeding, the effect of shift sensitivity may becomes more and more serious. In the following section, we will propose a new scheme to reduce the effect of aliasing terms, and thus reduce shift sensitivity.

## The nearly shift-invariant and critically-sampled DWT

In order to avoid redundancy, the framework of general DWT, Figure1, is remained in the proposed DWT except the wavelet filters are designed to satisfy some extra requirements.

### The extra requirements on wavelets

*H*

_{0}(

*z*) in Figure1 is designed to be expressed as (4) and satisfy (5).

*P*(

*z*) is a low-pass FIR filter;

*F*(·) and

*G*(·) denotes two FIR filters satisfy

Namely, the effect of aliasing terms in the low-pass subband has been approximately removed. The design scheme of wavelets that satisfy these extra requirements will be given in the next. First of all, it is necessary to review the design of biorthogonal wavelet.

### The design of biorthogonal wavelet

*H*

_{0}(

*z*) and${\stackrel{~}{H}}_{0}\left(z\right)$ in Figure1. In order to obtain smooth wavelet bases, it is always imposed certain numbers of zeros at

*z*= −1 for

*H*

_{0}(

*z*) and${\stackrel{~}{H}}_{0}\left(z\right)$, i.e., the filter has certain numbers of vanishing moments[14]. Suppose

*H*

_{0}(

*z*) is expressed as${H}_{0}\left(z\right)=\frac{1}{\sqrt{2}}{\left(\frac{1+{z}^{-1}}{2}\right)}^{k}Q\left(z\right)$, where$k\in {\mathbb{Z}}^{+}$,

*Q*(

*z*) is an odd symmetrical filters of (2

*l*+ 1)-tap (if

*Q*(

*z*) is a symmetric filter of (2

*l*+ 2)-tap, it can be expressed as$Q\left(z\right)=\frac{1+{z}^{-1}}{2}{Q}^{\prime}\left(z\right)$ with

*Q*

^{ ′ }(

*z*) a symmetric filter of (2

*l*+ 1)-tap). Similarly,${\stackrel{~}{H}}_{0}\left(z\right)$ is expressed as${\stackrel{~}{H}}_{0}\left(z\right)=\frac{1}{\sqrt{2}}{\left(\frac{1+{z}^{-1}}{2}\right)}^{\stackrel{~}{k}}\stackrel{~}{Q}\left(z\right)$, where$\stackrel{~}{Q}\left(z\right)$ is is a symmetric filter of$(2\stackrel{~}{l}+1)$-tap,$\stackrel{~}{k}\in {\mathbb{Z}}^{+}$. By introducing a suitable integer translation,

*Q*(

*z*) and$\stackrel{~}{Q}\left(z\right)$ can be expressed as

*Q*(

*z*) =

*q*(cos

*ω*) and$\stackrel{~}{Q}\left(z\right)=\stackrel{~}{q}\left(\mathit{\text{cos}}\omega \right)$, where

*q*(·) and$\stackrel{~}{q}(\xb7)$ are two polynomials of real coefficients. According to the solution in[15], we have

*y* = sin^{2}(*ω*/2),$N=(k+\stackrel{~}{k})/2$ (it means the vanishing moments of *H*_{0}(*z*) and${\stackrel{~}{H}}_{0}\left(z\right)$ must be either both odd or both even); *R*(·) is an odd polynomial, which is chosen such that$Q\left(z\right)\overline{\stackrel{~}{Q}\left(z\right)}\ge 0$ for all *ω*∈[0,*Π*.

We can obtain$l+\stackrel{~}{l}+1$ constraints about the coefficients of *Q*(*z*),$\stackrel{~}{Q}\left(z\right)$ and *R*(·)(Though there are$2l+2\stackrel{~}{l}+2$ equations on *Q*(*z*) and$\stackrel{~}{Q}\left(z\right)$, only$l+\stackrel{~}{l}+1$ of them are independent because of symmetry). It has been shown even when *R*(·) = 0, *Q*(*z*) and$\overline{\stackrel{~}{Q}\left(z\right)}$ may be not unique for a identical$Q\left(z\right)\overline{\stackrel{~}{Q}}\left(z\right)$. Furthermore, when *R*(·) ≠ 0, there are more choices in designing *Q*(*z*) and$\overline{\stackrel{~}{Q}\left(z\right)}$, so are *H*_{0}(*z*) and${\stackrel{~}{H}}_{0}\left(z\right)$. Therefore, it is possible to construct biorthogonal wavelet filters that satisfy the PR requirement and approximately satisfy (4) and (5). The design scheme is presented in the following section.

## The design of biorthogonal wavelets for the proposed DWT

*H*

_{0}(

*z*) =

*P*(

*z*)[

*F*(

*z*

^{2}) +

*z*

^{−1}

*G*(

*z*

^{2})]. On the other hand, the general expression of

*H*

_{0}(

*z*) is${H}_{0}\left(z\right)=\frac{1}{\sqrt{2}}{(1+{z}^{-1})}^{k}Q\left(z\right)$. Thus, let$P\left(z\right)=\frac{1}{\sqrt{2}}{(1+{z}^{-1})}^{k}$ (it is a low-pass filter),

*Q*(

*z*) =

*F*(

*z*

^{2}) +

*z*

^{−1}

*G*(

*z*

^{2}).

*F*(

*z*

^{2}) +

*z*

^{−1}

*G*(

*z*

^{2}) can be constructed in the following way. Suppose

*F*(

*z*) denotes a symmetrical filter of 2

*l*-tap

*f*,

*f*= [

*f*

_{l−1},…,

*f*

_{0},

*f*

_{0},…,

*f*

_{l−1}],

*G*(

*z*) denotes a (2

*l*−1)-tap symmetrical filter

*g*= [

*g*

_{l−1},…,

*g*

_{0},…,

*g*

_{l−1}], then [

*f*

_{l−1},

*g*

_{l−1},…,

*f*

_{0},

*g*

_{0},

*f*

_{0},…,

*g*

_{l−1},

*f*

_{l−1}] becomes a symmetrical filter of (4

*l*−1)-tap. This filter can be expressed as

*F*(

*z*

^{2}) +

*z*

^{−1}

*G*(

*z*

^{2}), where

*F*(

*z*) and

*G*(

*z*) are

*z*-transform of

*f*and

*g*, respectively. Let$\stackrel{~}{Q}\left(z\right)$ be a symmetrical filter of (2

*m*−1)-tap. After a suitable integer translation and some manipulations, we have

*a*

_{ n },

*n*= 0,…,2

*l*+

*m*−2, are the real coefficients depending on

*f*and

*g*. On the other hand, consider$y={sin}_{2}\frac{\omega}{2}=\frac{1}{2}-\frac{{z}^{-1}+z}{4}$, the right side of (7) can be simplified as following

*f*can be factorized as

*f*=

*f*

^{ ′ }⊗[1/2,1/2],${f}^{\prime}=[{f}_{l-1}^{\prime},\dots ,{f}_{0}^{\prime},\dots ,{f}_{l-1}^{\prime}]$ is a symmetrical filter, where ⊗ denotes the convolution operator. It means

*F*(

*z*) can be expressed as

*F*(

*z*) =

*z*

^{−1/2}cos(

*ω*/2)

*F*

^{ ′ }(

*z*). After a suitable translation,

*F*

^{ ′ }(

*z*) can be simplified as

*f*

^{ ′ }(cos

*ω*), where

*f*

^{ ′ }(·) a real coefficient polynomial. Similarly,

*g*= [

*q*

_{l−1},…,

*q*

_{0},…,

*q*

_{l−1}] can be expressed as a real coefficient polynomial

*g*(cos

*ω*). Therefore, if

*ω*/2)

*f*

^{ ′ }(cos

*ω*)| and |

*g*(cos

*ω*)| for all

*ω*∈[0,

*Π*]. It results

## The design and application examples

In this section, two design examples are presented to illustrate the design process, and three simulation examples are employed to demonstrate the performances of designed wavelets in the applications.

### The design examples

#### Example 1

Two biorthogonal that approximately satisfy (5) are designed. In the first bank, *F* and *G* are 5- and 4-tap, respectively, (*Q*(*z*) = *F*(*z*) + *z*^{−1}*G*(*z*) becomes 9-tap);$\stackrel{~}{Q}\left(z\right)$ is 3-tap;$k=11,\stackrel{~}{k}=1$ (it means *R*(·) = 0 in Equation (7)). *H*_{0}and${\stackrel{~}{H}}_{0}$ become 20- and 4-tap, respectively, which are identical with “bior3.9” wavelet filter banks in structure. The filter coefficients obtained are given in Table1. It can be verified that *H*_{0}and${\stackrel{~}{H}}_{0}$ satisfies the PR requirement (3) (Let${H}_{1}\left(z\right)=-\overline{{\stackrel{~}{H}}_{0}(-z)}$ and${\stackrel{~}{H}}_{1}\left(z\right)=-\overline{{H}_{0}(-z)}$). In the second filter bank, *H*_{0} and${\stackrel{~}{H}}_{0}$ are also 20- and 4-tap, however, we choose *R*(·) ≠ 0 to improve the approximation performance. The filter coefficients are shown in Table2.

*F*(

*z*) =

*z*

^{−1/2}cos(

*ω*/2)

*f*

^{ ′ }(cos

*ω*),

*G*(

*z*) =

*g*(cos

*ω*),$\mathrm{\angle F}\left(z\right)-\angle {z}^{-\frac{1}{2}}G\left(z\right)=0$, i.e., the requirement of phase offset is satisfied perfectly. The magnitude responds, cos(

*ω*/2)

*f*

^{ ′ }(cos

*ω*),

*g*(cos

*ω*), |cos(

*ω*/2)

*f*

^{ ′ }(cos

*ω*) +

*g*(cos

*ω*)| and |cos(

*ω*/2)

*f*

^{ ′ }(cos

*ω*)−

*g*(cos

*ω*)| of Table2 are depicted in Figure3a. The corresponding magnitude responds of wavelet “bior3.9” is depicted in Figure3b. It shows the designed wavelet has a better approximation performance to (6) than “bior3.9”.

### The application examples

#### Example 2

#### Example 3

In order to show the intensity difference after 2D wavelet analysis, the pixels whose intensities are smaller than a certain percent of *I*_{max} have been set as *I*_{min}, where *I*_{max} and *I*_{min} are the maximum and minimum of the LL subband coefficients, respectively. This operation is similar to the feature extraction according to the pixel intensities automatically. When the pixels in Figure5c,d whose intensities are smaller than 0.4*I*_{max} are set as *I*_{min}, Figure5c,d become Figure5e,f. It also illustrates the effect of shift sensitivity has been reduced efficiently using the designed wavelets.

#### Example 4

*Example 3*are performed to the two LL subbands: the pixels whose intensities are smaller than 0.55

*I*

_{max}are set as

*I*

_{min}. Figure6b shows the LL subband decomposed using the wavelet “bior3.9”, and (c) shows the one decomposed using the wavelet “bior3.9”. It also illustrates the effect of shift sensitivity has been reduced efficiently using the designed wavelets.

## Conclusion

For the general wavelets, the aliasing terms formed in analysis process of DWT can be eliminated in the synthesis process by the anti-aliasing properties of filter banks. However, the aliasing terms remain in the analysis outputs. In this article, it shows the aliasing terms cause the shift sensitivity of DWT. A novel scheme is proposed to reduce the effect of aliasing terms. Some extra requirements on the design of wavelets are proposed. The design scheme are presented and two biorthogonal wavelets that approximately satisfy the extra requirements have been designed. The shift sensitivity of the wavelet analysis has been reduced effectively by the new wavelets, which is very favorable for many signal processing applications, such as image registration, feature extraction and pattern recognition (the process is usually achieved using the analysis outputs rather than the synthesis results in these cases). The other superiority of the proposed wavelet analysis is that the wavelet representation remains critically-sampled and does not bring out any redundancy.

## Declarations

### Acknowledgements

This study had 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

- Abbate A, DeCusatis CM, Das PK:
*Wavelets and Subbands: Fundamentals and Applications*. 2002.View ArticleGoogle Scholar - Zavorin I, Moigne JL: Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery.
*IEEE Trans. Image Process*2005, 14(6):770-782.View ArticleGoogle Scholar - De Grandi G, Lee J-S, Schuler DL: Target detection and texture segmentation in polarimetric SAR images using a wavelet frame: theoretical aspects.
*IEEE Trans. Geosci. Remote Sens*2007, 45(11):3437-3453.View ArticleGoogle Scholar - Simoncelli EP, Freeman WT, Adelson EH, Heeger DJ: Shiftable Multiscale Transforms.
*IEEE Trans. Inf. Theory*1992, 38(2):587-607. 10.1109/18.119725MathSciNetView ArticleGoogle Scholar - Stone HS, Moigne JL, McGuire M: The translation sensitivity of wavelet-based registration.
*IEEE Trans. Pattern Anal. Mach. Intell*1999, 21: 1074-1081. 10.1109/34.799911View ArticleGoogle Scholar - Kingsbury BN: Image processing with complex wavelets.
*Phil. Trans. Roy. Soc. Lond*1999, 357(1760):2543-2560. 10.1098/rsta.1999.0447MATHView ArticleGoogle Scholar - Fernandes FCA, van Spaendonck RLC, Burrus CS: A New Framework for complex wavelet transforms.
*IEEE Trans. Signal Process*2003, 51(7):1825-1837. 10.1109/TSP.2003.812841MathSciNetView ArticleGoogle Scholar - Gopinath RA: The phaselet transform-an integral redundancy nearly shift-invariant wavelet transform.
*IEEE Trans. Signal Process*2003, 51(7):1792-1805. 10.1109/TSP.2003.812833MathSciNetView ArticleGoogle Scholar - Selesnick IW: The double-density dual-tree DWT.
*IEEE Trans. Signal Process*2004, 52(5):1304-1314. 10.1109/TSP.2004.826174MathSciNetView ArticleGoogle Scholar - Pun C-M, Lee M-C: Extraction of shift invariant wavelet features for classification of images with different sizes.
*IEEE Trans. Pattern Anal. Mach. Intell*2004, 26(9):1228-1233. 10.1109/TPAMI.2004.67View ArticleGoogle Scholar - Do MN, Vetterli M: The contourlet transform: an efficient directional multiresolution image representation.
*IEEE Trans. Image Process*2005, 14(12):2091-2106.MathSciNetView ArticleGoogle Scholar - Eslami R, Radha H: Translation-invariant Contourlet transform and its application to image denoising.
*IEEE Trans. Image Process*2006, 15(11):3362-3374.View ArticleGoogle Scholar - Ma J, Plonka G: The Curvelet transform.
*IEEE Signal Process. Mag*2010, 27(2):118-133.View ArticleGoogle Scholar - Daubechies I:
*Ten Lectures on Wavelets*. 1992.MATHView ArticleGoogle Scholar - Cohen A, Daubechies I, Feauveau J-C: Biorthogonal bases of compactly supported wavelets.
*Commun. Pure Appl. Math*1992, 45: 485-560. 10.1002/cpa.3160450502MATHMathSciNetView ArticleGoogle Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.