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Finite-memory denoising in impulsive noise using Gaussian mixture models
Yonina C. Eldar
*
,
Arie Yeredor
*
Corresponding author for this work
School of Electrical Engineering
IEEE
Tel Aviv University
Massachusetts Institute of Technology
Research output
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Article
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peer-review
6
Scopus citations
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Dive into the research topics of 'Finite-memory denoising in impulsive noise using Gaussian mixture models'. Together they form a unique fingerprint.
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Keyphrases
Gaussian Mixture Model
100%
Denoising
100%
Finite Memory
100%
Zero Mean
100%
Impulsive Noise
100%
Finite Memory Structure Filter
100%
Optimal Linear Estimator
50%
Minimum Mean Square Error
50%
Random Variables
50%
Computationally Efficient
50%
Gaussian Signals
50%
Nearly Optimal
50%
Gaussian Mixture
50%
Analytical Derivation
50%
Mean Squared Error
50%
Weighted Combination
50%
Optimal Estimator
50%
Gaussian Random Variable
50%
Gaussian Mixture Processes
50%
Linear Filter
50%
Optimal Linear Filter
50%
Efficient Architecture
50%
Colored Noise
50%
Engineering
Mean-Squared-Error
100%
Gaussian Mixture Model
100%
Gaussian Mixture
100%
Linear Filter
100%
Simulation Result
50%
Gaussians
50%
Random Variable ξ
50%
Static Case
50%
Optimal Estimator
50%
Gaussian Random Variable
50%
Colored Noise
50%
Linear Estimator
50%
Dynamic Case
50%
Mathematics
Gaussian Distribution
100%
Gaussian Mixture Model
100%
Squared Error
66%
Random Variable
33%
Fixed Number
33%
Optimal Estimator
33%
Gaussian Random Variable
33%
Colored Noise
33%