Model of Impulsive Noise for Data Transmission

Mertz, Pierre

doi:10.1109/tcom.1961.1097672

Cited by 75 publications

(9 citation statements)

References 4 publications

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“…In this sense they are non-transportable. Gaussian mixture and the hyperbolic distribution models are the most commonly used empirical models [1][2].…”

Section: Noise Modelmentioning

confidence: 99%

Vulnerability of Zigbee to impulsive noise in electricity substations

Bhatti

Shan

Atkinson

et al. 2011

2011 XXXth URSI General Assembly and Scientific Symposium

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“…In this sense they are non-transportable. Gaussian mixture and the hyperbolic distribution models are the most commonly used empirical models [1][2].…”

Section: Noise Modelmentioning

confidence: 99%

Vulnerability of Zigbee to impulsive noise in electricity substations

Bhatti

Shan

Atkinson

et al. 2011

2011 XXXth URSI General Assembly and Scientific Symposium

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“…Laplace noise arose again in a detection context in [8] where it was used a noise model for a study of polarity detection schemes. As noted in [6], the Laplace distribution is the limiting distribution of the Mertz model [9] for impulsive data transmission. In [lo] an empirically determined probability density function associated with certain subbands for certain images resembled a Laplace density.…”

Section: Introductionmentioning

confidence: 99%

Robust detection in nominally Laplace noise

Thompson

Halverson

Wise

1994

IEEE Trans. Commun.

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We consider the robust detection of a known constant signal in additive, mutually independent and identically distributed noise which is nominally Laplace. Employing the classical saddlepoint methodology of Huber, we specify the form of the robust detector and obtain closed form results which allow for the analysis of the performance of the robust detector. We show via examples that the robustness can entail a considerable cost in detector performance. In addition, we compare the performance of the robust detector to that of the Neyman-Pearson detector for examples where the noise is Laplace and also where the noise is least favorable for the given detector.

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“…It introduces sparse outliers in the gray-level distribution of the image besides Gaussian noise [9]. The performance of conventional restoration algorithms, which are particularly designed for noise of Gaussian model, will be highly degraded by such noise processes.…”

Section: Introductionmentioning

confidence: 99%

Mixed Gaussian-impulse video noise removal via temporal-spatial decomposition

Wang

Ling

et al. 2012

2012 IEEE International Symposium on Circuits and Systems

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This paper presents a novel denoising scheme for video sequences corrupted by mixed Gaussian-impulse noise. From a global viewpoint, such a video sequence contains three parts: temporal-spatially correlated video content, uncorrelated dense Gaussian noise, and uncorrelated sparse impulse noise. This fact motivates us to formulate the mixed Gaussian-impulse noise removal task as a temporal-spatial decomposition problem, which amounts to a convex program. A two-stage algorithm is developed to solve this problem efficiently. Effectiveness of the proposed algorithm on mixed Gaussian-impulse noise removal is validated through experiments. The results are satisfactory in both visual quality and PSNR values, while very few prior knowledge of noise statistic is required compared to most stateof-the-art methods.

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Model of Impulsive Noise for Data Transmission

Cited by 75 publications

References 4 publications

Vulnerability of Zigbee to impulsive noise in electricity substations

Vulnerability of Zigbee to impulsive noise in electricity substations

Robust detection in nominally Laplace noise

Mixed Gaussian-impulse video noise removal via temporal-spatial decomposition

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