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Signal Processing, IEEE Transactions on

Issue 7 • Date July 2003

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Displaying Results 1 - 25 of 31
  • A message from the editor-in-chief: best paper award recipients

    Page(s): 1681 - 1682
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    Freely Available from IEEE
  • Multiple signal extraction by multiple interference attenuation in the presence of random noise in seismic array data

    Page(s): 1683 - 1694
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (707 KB) |  | HTML iconHTML  

    A vector of digital filters is derived for the multichannel processing of the signals acquired by an array of sensors with the objective of extracting multiple desired signals by the attenuation of multiple interferences and random noise. The signals and interferences are assumed to have arbitrary waveforms with no a priori knowledge of these waveforms. The time duration of the recorded array data is assumed to be long enough to incorporate all time delayed propagated waveforms at the sensors of the array. The derivation is for the general case of an arbitrary array geometric configuration and is not confined to the special case of a linear array of equispaced sensors. The rationale adopted in the derivation of the filters is to give first priority at each discrete frequency to passing the signals, a second priority to canceling the interferences, and a third priority to attenuating the random noise. This rationale well suits the case of seismic data that are dominantly corrupted by strong interferences rather than random noise. Solving a constrained minimization problem derives the vector of array filters. The computation of this vector requires the application of a powerful matrix decomposition technique for the detection of any redundant and/or inconsistent constraints at each discrete frequency. The simulation results demonstrate the extraction ability of the derived filters in both the multiple input single output and the multiple input multiple output processing schemes. View full abstract»

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  • Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions

    Page(s): 1762 - 1770
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    We present an efficient particle filtering method to perform optimal estimation in jump Markov (nonlinear) systems (JMSs). Such processes consist of a mixture of heterogeneous models and possess a natural hierarchical structure. We take advantage of these specificities in order to develop a generic filtering methodology for these models. The method relies on an original and nontrivial combination of techniques that have been presented recently in the filtering literature, namely, the auxiliary particle filter and the unscented transform. This algorithm is applied to the complex problem of time-varying autoregressive estimation with an unknown time-varying model order. More precisely, we develop an attractive and original probabilistic model that relies on a flexible pole representation that easily lends itself to interpretations. We show that this problem can be formulated as a JMS and that the associated filtering problem can be efficiently addressed using the generic methodology developed in this paper. Simulations demonstrate the performance of our method compared to standard particle filtering techniques. View full abstract»

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  • Multistage vector quantizer optimization for packet networks

    Page(s): 1870 - 1879
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (686 KB) |  | HTML iconHTML  

    A multistage vector quantizer (MSVQ) based coding system is source-channel optimized for packet networks. Resilience to packet loss is enhanced by a proposed interleaving approach that ensures that a single lost packet only eliminates a subset of the vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. The new source-channel-optimized MSVQ is tested on memoryless speech line spectral frequency (LSF) parameter quantization as well as block-based image compression. With LSF coding, a source-channel-optimized MSVQ is shown to yield gains of up to 2.0 dB in signal-to-ratio (SNR) over traditional MSVQ and to substantially enhance the robustness of packetized speech transmission. Substantial gains were also obtained in the case of block-based image compression. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels. View full abstract»

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  • Hypercomplex correlation techniques for vector images

    Page(s): 1941 - 1953
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    Correlation techniques have been applied to almost every area of signal processing over the past century, yet their use has, in general, been limited to scalar signals. While there have been implementations in multichannel applications, these can be characterized as a combination of single channel processes. True vector correlation techniques, with global and interchannel measures, have only recently been demonstrated and are still in their infancy by comparison. This paper describes our work on vector correlation based on the use of hypercomplex Fourier transforms and presents, for the first time, a unified theory behind the information contained in the peak of a vector correlation response. By using example applications for color images, we also demonstrate some of the practical implications, together with our latest results. View full abstract»

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  • On the coefficient quantization of the Fourier basis

    Page(s): 1838 - 1845
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    A family of function bases is devised from a superposition of unit characteristic functions. It is shown that these simple bases can be used to perform a quantized Fourier transform. Furthermore, the transform can be implemented in an iterative scheme such that system resources are balanced in accordance with performance. View full abstract»

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  • An extended cyclic MUSIC algorithm

    Page(s): 1695 - 1701
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    Many modulated communication signals exhibit a cyclostationarity (or periodic correlation) property, corresponding to the underlying periodicity arising from carrier frequencies or baud rates. In this paper, we propose a new MUSIC-like direction-finding algorithm that exploits cyclostationarity in order to improve the direction-of-arrival (DOA) estimation. Both cyclic and conjugate cyclic correlation matrices are used in the proposed method so that it can be considered as an extension of the cyclic MUSIC algorithm . The proposed cyclic method allows an increase in resolution power and noise robustness. Depending on the modulation type of incoming signals, the proposed method is also able to handle more sources than the number of sensors. View full abstract»

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  • A block-iterative surrogate constraint splitting method for quadratic signal recovery

    Page(s): 1771 - 1782
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    A block-iterative parallel decomposition method is proposed to solve general quadratic signal recovery problems under convex constraints. The proposed method proceeds by local linearizations of blocks of constraints, and it is therefore not sensitive to their analytical complexity. In addition, it naturally lends itself to implementation on parallel computing architectures due to its flexible block-iterative structure. Comparisons with existing methods are carried out, and the case of inconsistent constraints is also discussed. Numerical results are presented. View full abstract»

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  • Blind adaptive joint multiuser detection and equalization in dispersive differentially encoded CDMA channels

    Page(s): 1880 - 1893
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1184 KB) |  | HTML iconHTML  

    The problem of blind adaptive joint multiuser detection and equalization in direct-sequence code division multiple access (DS/CDMA) systems operating over fading dispersive channels is considered. A blind and code-aided detection algorithm is proposed, i.e., the procedure requires knowledge of neither the interfering users' parameters (spreading codes, timing offsets, and propagation channels), nor the timing and channel impulse response of the user of interest but only of its spreading code. The proposed structure is a two-stage one: the first stage is aimed at suppressing the multiuser interference, whereas the second-stage performs channel estimation and data detection. Special attention is paid to theoretical issues concerning the design of the interference blocking stage and, in particular, to the development of general conditions to prevent signal cancellation under vanishingly small noise. A statistical analysis of the proposed system is also presented, showing that it incurs a very limited loss with respect to the nonblind minimum mean square error detector, outperforms other previously known blind systems, and is near-far resistant. A major advantage of the new structure is that it admits an adaptive implementation with quadratic (in the processing gain) computational complexity. This adaptive algorithm, which couples a recursive-least-squares estimation of the blocking matrix and subspace tracking techniques, achieves effective steady-state performance. View full abstract»

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  • A new framework for complex wavelet transforms

    Page(s): 1825 - 1837
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce two-stage mapping-based complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, non-redundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and non-redundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we can exploit this flexibility to create the complex double-density DWT (CDDWT): a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3m-1/2m-1) in m dimensions. To the best of our knowledge, no other transform achieves all these properties at a lower redundancy. View full abstract»

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  • Bussgang blind deconvolution for impulsive signals

    Page(s): 1905 - 1915
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    Many blind deconvolution algorithms have been designed to extract digital communications signals corrupted by intersymbol interference (ISI). Such algorithms generally fail when applied to signals with impulsive characteristics, such as acoustic signals. While it is possible to stabilize such procedures in many cases by imposing unit-norm constraints on the adaptive equalizer coefficient vector, these modifications require costly divide and square-root operations. In this paper, we provide a theoretical analysis and explanation as to why unconstrained Bussgang-type algorithms are generally unsuitable for deconvolving impulsive signals. We then propose a novel modification of one such algorithm (the Sato algorithm) to enable it to deconvolve such signals. Our approach maintains the algorithmic simplicity of the Sato algorithm, requiring only additional multiplies and adds to implement. Sufficient conditions on the source signal distribution to guarantee local stability of the modified Sato algorithm about a deconvolving solution are derived. Computer simulations show the efficiency of the proposed approach as compared with various constrained and unconstrained blind deconvolution algorithms when deconvolving impulsive signals. View full abstract»

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  • Performance analysis of the sign algorithm for a constrained adaptive IIR notch filter

    Page(s): 1846 - 1858
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    Adaptive infinite impulse response (IIR) notch filters are very attractive in terms of their reasonable performances and low computational requirements. Generally, it is very difficult to assess their performances analytically due to their IIR nature. This paper analyzes in detail the steady-state performance of the sign algorithm (SA) for a well-known adaptive IIR notch filter with constrained poles and zeros. Slow adaptation and Gaussianity of the notch filter output are assumed for the sake of analysis. Two difference equations are first established for the convergences in the mean and mean square in the vicinity of the steady state of the algorithm. Steady-state estimation error or bias and mean square error (MSE) of the SA are then derived in closed forms. A coarse stability bound is also derived for the algorithm. Theory-based comparison between the algorithm and the plain gradient (PG) algorithm is done in some detail. Extensive simulations are conducted to demonstrate the validity of the analytical results for both slow and relatively fast adaptations. View full abstract»

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  • On robust Capon beamforming and diagonal loading

    Page(s): 1702 - 1715
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (748 KB) |  | HTML iconHTML  

    The Capon (1969) beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. We show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches, but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance for SOI power estimation is demonstrated via a number of numerical examples. View full abstract»

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  • Convergence properties and data efficiency of the minimum error entropy criterion in ADALINE training

    Page(s): 1966 - 1978
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    Recently, we have proposed the minimum error entropy (MEE) criterion as an information theoretic alternative to the widely used mean square error criterion in supervised adaptive system training. For this purpose, we have formulated a nonparametric estimator for Renyi's entropy that employs Parzen windowing. Mathematical investigation of the proposed entropy estimator revealed interesting insights about the process of information theoretical learning. This new estimator and the associated criteria have been applied to the supervised and unsupervised training of adaptive systems in a wide range of problems successfully. In this paper, we analyze the structure of the MEE performance surface around the optimal solution, and we derive the upper bound for the step size in adaptive linear neuron (ADALINE) training with the steepest descent algorithm using MEE. In addition, the effects of the entropy order and the kernel size in Parzen windowing on the shape of the performance surface and the eigenvalues of the Hessian at and around the optimal solution are investigated. Conclusions from the theoretical analyses are illustrated through numerical examples. View full abstract»

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  • A general-purpose optimization approach for designing two-channel FIR filterbanks

    Page(s): 1783 - 1791
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (757 KB) |  | HTML iconHTML  

    An efficient general-purpose optimization approach is proposed for designing two-channel finite impulse response (FIR) filterbanks. This technique can be used for optimizing two-channel FIR filterbanks in all alias-free cases proposed in the literature. The generalized problem is to minimize the maximum of the stopband energies of the two analysis filters subject to the given passband and transition band constraints and the given allowable reconstruction error. Therefore, in addition to the perfect-reconstruction filterbanks, nearly perfect-reconstruction banks can be optimized in a controlled manner. The optimization is carried out in two steps. In the first step, for the selected type of the filterbank, a good starting-point filterbank for further optimization is generated using an existing design scheme. The second step involves optimizing the filterbank with the aid of a modified Dutta-Vidyasagar (1977) algorithm. Several examples are included, illustrating the efficiency and the flexibility of the proposed approach. View full abstract»

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  • Optimal weights computation of an emitting antenna array - the Obele algorithm

    Page(s): 1716 - 1721
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    The capacity of a mobile network can be increased by using antenna arrays. We present a new method that jointly optimizes antenna weights and powers for all users on the downlink on a single cell. The algorithm, which only requires the estimation of the noise power σ2 and knowledge of the channel responses on the downlink, consists of minimizing the greatest eigenvalue of a matrix. This optimization results from an iterative process. In a first pass, we compute the greatest eigenvalue of a matrix - the power is given by the associated eigenvector - which depends on the antenna weights, and in a second pass, we compute a new set of antenna weights that will be reused in the next iteration. We prove that it converges to the global minimum. View full abstract»

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  • Detection of LSB steganography via sample pair analysis

    Page(s): 1995 - 2007
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB) |  | HTML iconHTML  

    This paper introduces a new, principled approach to detecting least significant bit (LSB) steganography in digital signals such as images and audio. It is shown that the length of hidden messages embedded in the least significant bits of signal samples can be estimated with relatively high precision. The new steganalytic approach is based on some statistical measures of sample pairs that are highly sensitive to LSB embedding operations. The resulting detection algorithm is simple and fast. To evaluate the robustness of the proposed steganalytic approach, bounds on estimation errors are developed. Furthermore, the vulnerability of the new approach to possible attacks is also assessed, and counter measures are suggested. View full abstract»

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  • Robust filtering for uncertain nonlinearly parameterized plants

    Page(s): 1806 - 1815
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (683 KB) |  | HTML iconHTML  

    We address the robust filtering problem for a wide class of systems whose state-space data assume a very general nonlinear dependence in the uncertain parameters. Our resolution methods rely on new linear matrix inequality characterizations of H2 and H performances, which, in conjunction with suitable linearization transformations of the variables, give rise to practical and computationally tractable formulations for the robust filtering problem. View full abstract»

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  • Detection performance of the reduced-rank linear predictor ROCKET

    Page(s): 1731 - 1738
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (449 KB) |  | HTML iconHTML  

    This paper assesses the frequency detection capabilities of a new signal-dependent reduced-rank linear predictor applied to autoregressive spectrum estimation. The new technique is called reduced-order correlation kernel estimation technique (ROCKET). Its detection performance is examined by comparison to a full-rank autoregressive (FR-AR) estimator and two reduced-rank principal component autoregressive (PC-AR) estimators based on both the standard signal-independent version and a modified signal-dependent method. The performance of the new autoregressive estimator is also compared as a function of rank to the popular pseudo-spectrum estimator MUSIC. The performance metrics examined are the probability of detection (PD) and the false alarm rate (FAR) of detecting the spatial frequencies of plane waves impinging on a uniform line array in additive white Gaussian noise. These metrics are studied as a function of subspace rank, sample support, and signal-to-noise ratio (SNR). Simulations show that the signal-dependent reduced-rank estimators significantly outperform both the signal-independent version of PC-AR and the FR-AR estimator for low sample support and low SNR environments. One notable characteristic of ROCKET that highlights its distinct subspace selection is its performance as a function of subspace rank. It is observed that for equal powered signals, its peak performance is nearly invariant to signal rank and that at almost any subspace rank ROCKET meets or exceeds FR-AR performance. This provides an extra degree of robustness when the signal rank is unknown. View full abstract»

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  • Fractional biorthogonal partners in channel equalization and signal interpolation

    Page(s): 1928 - 1940
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    The concept of biorthogonal partners has been introduced recently by the authors. The work presented here is an extension of some of these results to the case where the upsampling and downsampling ratios are not integers but rational numbers, hence, the name fractional biorthogonal partners. The conditions for the existence of stable and of finite impulse response (FIR) fractional biorthogonal partners are derived. It is also shown that the FIR solutions (when they exist) are not unique. This property is further explored in one of the applications of fractional biorthogonal partners, namely, the fractionally spaced equalization in digital communications. The goal is to construct zero-forcing equalizers (ZFEs) that also combat the channel noise. The performance of these equalizers is assessed through computer simulations. Another application considered is the all-FIR interpolation technique with the minimum amount of oversampling required in the input signal. We also consider the extension of the least squares approximation problem to the setting of fractional biorthogonal partners. View full abstract»

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  • A new time delay estimator based on ETDE

    Page(s): 1859 - 1869
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    In this paper, we address the problem of online subsample time delay estimation of narrowband signals of known center frequency. We propose a new so-called mixed modulated Lagrange explicit time delay estimation (MMLETDE) algorithm and study its performance through computer simulations. The MMLETDE is a modulated Lagrange ETDE, but the delay estimate adaptation process is based on the truncated sinc fractional delay filter algorithm. We provide theoretical derivations for our proposed estimator, a proof of its convergence performance, learning characteristics of its error performance surface, and an expression for its delay variance. Using simulations, we show that MMLETDE requires only a small filter order and has no noticeable estimation bias over a wide frequency range. View full abstract»

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  • H2 optimal linear robust sampled-data filtering design using polynomial approach

    Page(s): 1816 - 1824
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (706 KB) |  | HTML iconHTML  

    A new frequency domain approach to robust multi-input-multi-output (MIMO) linear filter design for sampled-data systems is presented. The system and noise models are assumed to be represented by polynomial forms that are not perfectly known except that they belong to a certain set. The optimal design guarantees that the error variance is kept below an upper bound that is minimized for all admissible uncertainties. The design problem is cast in the context of H2 via the polynomial matrix representation of systems with norm bounded unstructured uncertainties. The sampled-data mix of continuous and discrete time systems is handled by means of a lifting technique; however, it does not increase the dimensionality or alter the computational cost of the solution. The setup adopted allows dealing with several filtering problems. A simple deconvolution example illustrates the procedure. View full abstract»

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  • Performance analysis of correlation-based watermarking schemes employing Markov chaotic sequences

    Page(s): 1979 - 1994
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (857 KB) |  | HTML iconHTML  

    In this paper, theoretical performance analysis of watermarking schemes based on correlation detection is undertaken, leading to a number of important observations on the watermarking system detection performance. Statistical properties of watermark sequences generated by piecewise-linear Markov maps are investigated. Correlation/spectral properties of such sequences are easily controllable, which is a fact that reflects on the watermarking system performance. A family of chaotic maps, namely the skew tent map family, is used to verify the theoretical analysis. Skew tent chaotic sequences are compared against the widely used pseudorandom sequences, indicating the superiority of the former in watermarking applications. The minimum number of samples required for reliable watermark detection is also investigated. Experiments using audio data are conducted to verify the theoretical analysis results. View full abstract»

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  • A semi-blind channel estimation technique based on second-order blind method for CDMA systems

    Page(s): 1894 - 1904
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    This paper aims at studying a semi-blind channel estimation scheme based on the subspace method or a carefully weighted linear prediction approach. The corresponding (composite) semi-blind cost functions result from a linear combination of the training-based cost function and a blind cost function. For each blind method, we show how to calculate the asymptotic estimation error. Therefore, by minimizing this error, we can properly tune the K-dimensional regularizing vector introduced in the composite semi-blind criterion (for K active users in the uplink). The asymptotic estimation error minimization is a K-variable minimization problem, which is a complex issue with which to deal. We explicitly show under what conditions this problem boils down to K single-variable minimization problems. Our discussion is not limited to theoretical analyses. Simulation results performed in a realistic context [Universal Mobile Telecommunication System-time division duplex (UMTS-TDD) mode] are provided. In particular, we conclude about the potential of the proposed approach in real communication systems. View full abstract»

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  • The phaselet transform-an integral redundancy nearly shift-invariant wavelet transform

    Page(s): 1792 - 1805
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1172 KB) |  | HTML iconHTML  

    This paper introduces an approximately shift invariant redundant dyadic wavelet transform - the phaselet transform - that includes the popular dual-tree complex wavelet transform of Kingsbury (see Phil. R. Soc. London A, Sept. 1999) as a special case. The main idea is to use a finite set of wavelets that are related to each other in a special way - and hence called phaselets - to achieve approximate shift-redundancy; the bigger the set, the better the approximation. A sufficient condition on the associated scaling filters to achieve this is that they are fractional shifts of each other. Algorithms for the design of phaselets with a fixed number vanishing moments is presented - building on the work of Selesnick (see IEEE Trans. Signal Processing) for the design of wavelet pairs for Kingsbury's dual-tree complex wavelet transform. Construction of two-dimensional (2-D) directional bases from tensor products of one-dimensional (1-D) phaselets is also described. Phaselets as a new approach to redundant wavelet transforms and their construction are both novel and should be interesting to the reader, independent of the approximate shift invariance property that this paper argues they possess. View full abstract»

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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Meet Our Editors

Editor-in-Chief
Zhi-Quan (Tom) Luo
University of Minnesota