A new gaussian noise filter based on interval type-2 fuzzy logic systems

S. T. Wang; Fu Lai Korris Chung; Y. Y. Li; D. W. Hu; X. S. Wu

doi:10.1007/s00500-004-0362-y

A new gaussian noise filter based on interval type-2 fuzzy logic systems

S. T. Wang, Fu Lai Korris Chung, Y. Y. Li, D. W. Hu, X. S. Wu

Research output: Journal article publication › Journal article › Academic research › peer-review

14 Citations (Scopus)

Abstract

In this paper, a new selective feedback fuzzy neural network (SFNN) based on interval type-2 fuzzy logic systems is introduced by partitioning input and output spaces and based upon which a new FLS filter is further studied. The experimental results demonstrate that this new FLS filter outperforms other filters (e.g. the mean filter and the Wiener filter) in suppressing Gaussian noise and maintaining the original structure of an image.

Original language	English
Pages (from-to)	398-406
Number of pages	9
Journal	Soft Computing
Volume	9
Issue number	5
DOIs	https://doi.org/10.1007/s00500-004-0362-y
Publication status	Published - 1 May 2005

Keywords

Filter
Fuzzy logic systems
Gaussian noise
Image-processing
Neural networks
Type-2 fuzzy sets

ASJC Scopus subject areas

Software
Theoretical Computer Science
Geometry and Topology

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10.1007/s00500-004-0362-y

Cite this

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AB - In this paper, a new selective feedback fuzzy neural network (SFNN) based on interval type-2 fuzzy logic systems is introduced by partitioning input and output spaces and based upon which a new FLS filter is further studied. The experimental results demonstrate that this new FLS filter outperforms other filters (e.g. the mean filter and the Wiener filter) in suppressing Gaussian noise and maintaining the original structure of an image.

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KW - Fuzzy logic systems

KW - Gaussian noise

KW - Image-processing

KW - Neural networks

KW - Type-2 fuzzy sets

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A new gaussian noise filter based on interval type-2 fuzzy logic systems

Abstract

Keywords

ASJC Scopus subject areas

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