A functional link network with higher order statistics for signal enhancement

Bor-Shyh Lin; Bor Shing Lin; Fok Ching Chong; Feipei Lai

doi:10.1109/TSP.2006.882075

A functional link network with higher order statistics for signal enhancement

Bor-Shyh Lin, Bor Shing Lin, Fok Ching Chong, Feipei Lai

Institute of Imaging and Biomedical Photonics

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

A functional link network with higher order statistics is introduced for signal enhancement. The proposed scheme uses the mean-square error (MSE) between higher order statistics of desired signals and filtered output as the learning criterion for training weights in the functional link network. This is motivated by the fact that higher order statistics have a natural tolerance to Gaussian and symmetrically distributed non-Gaussian noises. Results show that the performance of functional link network with higher order statistics is less sensitive to the selection of learning rates than the conventional functional link network and adaptive line enhancement. It is also demonstrated that it can enhance signal more effectively under different noise levels for stationary and nonstationary Gaussian noises.

Original language	English
Pages (from-to)	4821-4826
Number of pages	6
Journal	IEEE Transactions on Signal Processing
Volume	54
Issue number	12
DOIs	https://doi.org/10.1109/TSP.2006.882075
State	Published - Dec 2006

Keywords

Functional link network
Higher order statistics
Signal enhancement

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10.1109/TSP.2006.882075

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title = "A functional link network with higher order statistics for signal enhancement",

abstract = "A functional link network with higher order statistics is introduced for signal enhancement. The proposed scheme uses the mean-square error (MSE) between higher order statistics of desired signals and filtered output as the learning criterion for training weights in the functional link network. This is motivated by the fact that higher order statistics have a natural tolerance to Gaussian and symmetrically distributed non-Gaussian noises. Results show that the performance of functional link network with higher order statistics is less sensitive to the selection of learning rates than the conventional functional link network and adaptive line enhancement. It is also demonstrated that it can enhance signal more effectively under different noise levels for stationary and nonstationary Gaussian noises.",

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AU - Lin, Bor-Shyh

AU - Lin, Bor Shing

AU - Chong, Fok Ching

AU - Lai, Feipei

PY - 2006/12

Y1 - 2006/12

N2 - A functional link network with higher order statistics is introduced for signal enhancement. The proposed scheme uses the mean-square error (MSE) between higher order statistics of desired signals and filtered output as the learning criterion for training weights in the functional link network. This is motivated by the fact that higher order statistics have a natural tolerance to Gaussian and symmetrically distributed non-Gaussian noises. Results show that the performance of functional link network with higher order statistics is less sensitive to the selection of learning rates than the conventional functional link network and adaptive line enhancement. It is also demonstrated that it can enhance signal more effectively under different noise levels for stationary and nonstationary Gaussian noises.

AB - A functional link network with higher order statistics is introduced for signal enhancement. The proposed scheme uses the mean-square error (MSE) between higher order statistics of desired signals and filtered output as the learning criterion for training weights in the functional link network. This is motivated by the fact that higher order statistics have a natural tolerance to Gaussian and symmetrically distributed non-Gaussian noises. Results show that the performance of functional link network with higher order statistics is less sensitive to the selection of learning rates than the conventional functional link network and adaptive line enhancement. It is also demonstrated that it can enhance signal more effectively under different noise levels for stationary and nonstationary Gaussian noises.

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JO - IEEE Transactions on Signal Processing

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A functional link network with higher order statistics for signal enhancement

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Keywords

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