The Important Properties and Applications of the Adaptive Weighted Fuzzy Mean Filter

Chang Shing Lee, Yau-Hwang Kuo

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The important properties and applications of the adaptive weighted fuzzy mean (AWFM) filter are presented in this paper. AWFM is an extension of the weighted fuzzy mean (WFM) filter to overcome the drawback of WFM in fine signal preservation. It not only preserves the high performance of WFM on heavy additive impulse noise, but also improves the efficiency of WFM on removing light additive impulse noise. Some deterministic and statistical properties of the AWFM filter are analyzed, and the main characteristic of the AWFM filter that maps the input signal space into a root signal space, where a root signal is an invariant signal to the filter, is also discussed. Compared with the other filters, AWFM exhibits better performance in the criteria of mean absolute error and mean square error. On the subjective evaluation of those filtered images, AWFM also results in a higher quality of global restoration.

Original languageEnglish
Pages (from-to)253-274
Number of pages22
JournalInternational Journal of Intelligent Systems
Volume14
Issue number2-3
Publication statusPublished - 1999 Mar

Fingerprint

Impulse noise
Filter
Adaptive filters
Mean square error
Restoration
Impulse Noise
Roots
Subjective Evaluation
Adaptive Filter
Preservation
Statistical property
High Performance

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Human-Computer Interaction
  • Artificial Intelligence

Cite this

@article{ebdbd92ea8544958b2bca3ddcf59edac,
title = "The Important Properties and Applications of the Adaptive Weighted Fuzzy Mean Filter",
abstract = "The important properties and applications of the adaptive weighted fuzzy mean (AWFM) filter are presented in this paper. AWFM is an extension of the weighted fuzzy mean (WFM) filter to overcome the drawback of WFM in fine signal preservation. It not only preserves the high performance of WFM on heavy additive impulse noise, but also improves the efficiency of WFM on removing light additive impulse noise. Some deterministic and statistical properties of the AWFM filter are analyzed, and the main characteristic of the AWFM filter that maps the input signal space into a root signal space, where a root signal is an invariant signal to the filter, is also discussed. Compared with the other filters, AWFM exhibits better performance in the criteria of mean absolute error and mean square error. On the subjective evaluation of those filtered images, AWFM also results in a higher quality of global restoration.",
author = "Lee, {Chang Shing} and Yau-Hwang Kuo",
year = "1999",
month = "3",
language = "English",
volume = "14",
pages = "253--274",
journal = "International Journal of Intelligent Systems",
issn = "0884-8173",
publisher = "John Wiley and Sons Ltd",
number = "2-3",

}

The Important Properties and Applications of the Adaptive Weighted Fuzzy Mean Filter. / Lee, Chang Shing; Kuo, Yau-Hwang.

In: International Journal of Intelligent Systems, Vol. 14, No. 2-3, 03.1999, p. 253-274.

Research output: Contribution to journalArticle

TY - JOUR

T1 - The Important Properties and Applications of the Adaptive Weighted Fuzzy Mean Filter

AU - Lee, Chang Shing

AU - Kuo, Yau-Hwang

PY - 1999/3

Y1 - 1999/3

N2 - The important properties and applications of the adaptive weighted fuzzy mean (AWFM) filter are presented in this paper. AWFM is an extension of the weighted fuzzy mean (WFM) filter to overcome the drawback of WFM in fine signal preservation. It not only preserves the high performance of WFM on heavy additive impulse noise, but also improves the efficiency of WFM on removing light additive impulse noise. Some deterministic and statistical properties of the AWFM filter are analyzed, and the main characteristic of the AWFM filter that maps the input signal space into a root signal space, where a root signal is an invariant signal to the filter, is also discussed. Compared with the other filters, AWFM exhibits better performance in the criteria of mean absolute error and mean square error. On the subjective evaluation of those filtered images, AWFM also results in a higher quality of global restoration.

AB - The important properties and applications of the adaptive weighted fuzzy mean (AWFM) filter are presented in this paper. AWFM is an extension of the weighted fuzzy mean (WFM) filter to overcome the drawback of WFM in fine signal preservation. It not only preserves the high performance of WFM on heavy additive impulse noise, but also improves the efficiency of WFM on removing light additive impulse noise. Some deterministic and statistical properties of the AWFM filter are analyzed, and the main characteristic of the AWFM filter that maps the input signal space into a root signal space, where a root signal is an invariant signal to the filter, is also discussed. Compared with the other filters, AWFM exhibits better performance in the criteria of mean absolute error and mean square error. On the subjective evaluation of those filtered images, AWFM also results in a higher quality of global restoration.

UR - http://www.scopus.com/inward/record.url?scp=0033097311&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033097311&partnerID=8YFLogxK

M3 - Article

VL - 14

SP - 253

EP - 274

JO - International Journal of Intelligent Systems

JF - International Journal of Intelligent Systems

SN - 0884-8173

IS - 2-3

ER -