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Signal Processing Letters, IEEE

Issue 1 • Date Jan. 1998

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Displaying Results 1 - 8 of 8
  • Iterative image reconstruction: a wavelet approach

    Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (88 KB)  

    Image reconstruction from the measurements of the image Fourier transform magnitude remains an important and difficult problem. Among all the approaches developed to solve this problem, the iterative transform algorithms are currently the most efficient. However, these algorithms suffer from major drawbacks that limit their practical application. We introduce a wavelet adaptation of the general iterative algorithm that can significantly improve the performance of the algorithm while dramatically reducing its computational complexity. View full abstract»

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  • Restoration of impulse noise corrupted images using long-range correlation

    Page(s): 4 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (122 KB)  

    We present a new algorithm that can remove impulse noise from corrupted images while preserving details. The algorithm is fundamentally different from the traditional methods in that it can utilize information not just of a local window centered about the corrupted pixel, but also of some remote regions in the image. Computer simulations indicate that our algorithm outperforms many existing techniques. View full abstract»

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  • Statistical linear approximation for environment compensation

    Page(s): 8 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (95 KB)  

    The statistical linear approximation (SLA) method is proposed as a novel way to approximate a nonlinear function by a linearized model. In the proposed method, an optimization criterion for approximation is defined in terms of statistical expectation. The SLA is applied to environment compensation where the speech contamination rule appears as a highly nonlinear function of the relevant variables. View full abstract»

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  • Broadband active noise compressor

    Page(s): 11 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (105 KB)  

    A broadband active noise compressor (ANCP) is presented to adapt the active noise equalization (ANE) technique suitable for practical usage. Compared to the existing broadband ANE system, the novel ANCP not only has the ability to shape the residual noise spectrum, but can also automatically adjust the residual noise power. This algorithm is analyzed in steady state and verified by computer simulations. View full abstract»

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  • A parametric modeling approach to Hilbert transformation

    Page(s): 15 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (98 KB)  

    A parametric approach to compute the Hilbert transform (HT) of a real signal is proposed. A scaled version of the real signal is raised to the exponent of Napier's base, and an all-pole signal model is fitted to approximate the resulting signal as though it were the envelope. An error criterion that flattens this envelope is then minimized over a set of complex Fourier coefficients. The resulting approximation is guaranteed to be a minimum-phase (MinP) signal. Since a MinP signal has the property that the logarithm of its envelope and its phase are related by the HT, it follows that the resulting phase of MinP approximation yields the desired HT. View full abstract»

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  • Normalized adaptive decision directed equalization

    Page(s): 18 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (62 KB)  

    It has been observed that with appropriate stepsize normalization, the convergence speed of the constant modulus (CM) algorithm can be dramatically improved. It is shown that if a different normalization strategy is used, one that takes into account the finite alphabet structure of the signals, a standard normalized version of the decision directed equalizer (DDE) is achieved. A simulation example is included to demonstrate the faster convergence of the normalized DDE compared with its constant stepsize implementation and the normalized CM. View full abstract»

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  • Stochastic maximum likelihood methods for semi-blind channel estimation

    Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (156 KB)  

    A blind stochastic maximum likelihood (ML) channel estimation algorithm is adapted to incorporate a known training sequence as part of the transmitted frame. A hidden Markov model (HMM) formulation of the problem is introduced, and the Baum-Welch (1970) algorithm is modified to provide a computationally efficient solution to the resulting optimization problem. The proposed method provides a unified framework for semi-blind channel estimation, which exploits information from both the training and the blind part of the received data record. The performance of the ML estimator is studied, based on the evaluation of Cramer-Rao bounds (CRBs). Finally, some preliminary simulation results are presented. View full abstract»

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  • Dynamic behavior of the whitening process

    Page(s): 25 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (94 KB)  

    We present an insightful view of whitening processes (or orthogonalization processes), which occur frequently in signal processing and neural network applications. For such processes, we show the dependence of the limiting solution on the initial matrix and the precise convergence rate. Simulation results corroborate our theoretical analysis. View full abstract»

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Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Meet Our Editors

Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu