Estimation of optimal parameter in ε-filter based on signal-noise decorrelation

Mitsuharu Matsumoto; Shuji Hashimoto

doi:10.1587/transinf.E92.D.1312

Estimation of optimal parameter in ε-filter based on signal-noise decorrelation

Mitsuharu Matsumoto^*, Shuji Hashimoto

^*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

ε-filter is a nonlinear filter for reducing noise and is applicable not only to speech signals but also to image signals. The filter design is simple and it can effectively reduce noise with an adequate filter parameter. This paper presents a method for estimating the optimal filter parameter of ε-filter based on signal-noise decorrelation and shows that it yields the optimal filter parameter concerning a wide range of noise levels. The proposed method is applicable where the noise to be removed is uncorrelated with signal, and it does not require any other knowledge such as noise variance and training data.

Original language	English
Pages (from-to)	1312-1315
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E92-D
Issue number	6
DOIs	https://doi.org/10.1587/transinf.E92.D.1312
Publication status	Published - 2009

Keywords

ε-filter
Nonlinear filter
Parameter optimization

ASJC Scopus subject areas

Electrical and Electronic Engineering
Software
Artificial Intelligence
Hardware and Architecture
Computer Vision and Pattern Recognition

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10.1587/transinf.E92.D.1312

Cite this

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AB - ε-filter is a nonlinear filter for reducing noise and is applicable not only to speech signals but also to image signals. The filter design is simple and it can effectively reduce noise with an adequate filter parameter. This paper presents a method for estimating the optimal filter parameter of ε-filter based on signal-noise decorrelation and shows that it yields the optimal filter parameter concerning a wide range of noise levels. The proposed method is applicable where the noise to be removed is uncorrelated with signal, and it does not require any other knowledge such as noise variance and training data.

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KW - Nonlinear filter

KW - Parameter optimization

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Estimation of optimal parameter in ε-filter based on signal-noise decorrelation

Abstract

Keywords

ASJC Scopus subject areas

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