Intelligent fuzzy image filter for impulse noise removal

Chang Shing Lee, Chin Yuan Hsu, Yau-Hwang Kuo

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper proposes an intelligent Fuzzy Image Filter (FIF) to remove impulse noise. The filter including two processes, the Intelligent Fuzzy Number Deciding (IFND) process and fuzzy inference process, to filter impulse noise from heavily corrupted images efficiently. IFN D can automatically decide the number of fuzzy number based on image features to overcome the drawbacks of Adaptive Weighted Fuzzy Mean (AWFM) filter that must be defined by domain expert. Moreover, the fuzzy inference process refers the knowledge base produced by INFD and fuzzy rule base that can improve the weakness of conventional filters in heavily corrupted condition. The intelligent FIF achieves better performance than the other filters based on the criteria of Mean Absolute Error (MAE), and Mean Square Error (MSE). By the experiments, FIF still keeps the high performance to filtering impulse noise from color image.

Original languageEnglish
Pages (from-to)431-436
Number of pages6
JournalIEEE International Conference on Fuzzy Systems
Volume1
Publication statusPublished - 2002

Fingerprint

image filters
Impulse Noise
Noise Removal
Impulse noise
impulses
Fuzzy inference
Filter
filters
inference
Fuzzy rules
Mean square error
Fuzzy Inference
Fuzzy numbers
Color
Fuzzy Rule Base
color
Color Image
Experiments
Knowledge Base
Filtering

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics

Cite this

@article{2d9fb4dfb3c34963ae1425950fdadba0,
title = "Intelligent fuzzy image filter for impulse noise removal",
abstract = "This paper proposes an intelligent Fuzzy Image Filter (FIF) to remove impulse noise. The filter including two processes, the Intelligent Fuzzy Number Deciding (IFND) process and fuzzy inference process, to filter impulse noise from heavily corrupted images efficiently. IFN D can automatically decide the number of fuzzy number based on image features to overcome the drawbacks of Adaptive Weighted Fuzzy Mean (AWFM) filter that must be defined by domain expert. Moreover, the fuzzy inference process refers the knowledge base produced by INFD and fuzzy rule base that can improve the weakness of conventional filters in heavily corrupted condition. The intelligent FIF achieves better performance than the other filters based on the criteria of Mean Absolute Error (MAE), and Mean Square Error (MSE). By the experiments, FIF still keeps the high performance to filtering impulse noise from color image.",
author = "Lee, {Chang Shing} and Hsu, {Chin Yuan} and Yau-Hwang Kuo",
year = "2002",
language = "English",
volume = "1",
pages = "431--436",
journal = "IEEE International Conference on Fuzzy Systems",
issn = "1098-7584",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Intelligent fuzzy image filter for impulse noise removal. / Lee, Chang Shing; Hsu, Chin Yuan; Kuo, Yau-Hwang.

In: IEEE International Conference on Fuzzy Systems, Vol. 1, 2002, p. 431-436.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Intelligent fuzzy image filter for impulse noise removal

AU - Lee, Chang Shing

AU - Hsu, Chin Yuan

AU - Kuo, Yau-Hwang

PY - 2002

Y1 - 2002

N2 - This paper proposes an intelligent Fuzzy Image Filter (FIF) to remove impulse noise. The filter including two processes, the Intelligent Fuzzy Number Deciding (IFND) process and fuzzy inference process, to filter impulse noise from heavily corrupted images efficiently. IFN D can automatically decide the number of fuzzy number based on image features to overcome the drawbacks of Adaptive Weighted Fuzzy Mean (AWFM) filter that must be defined by domain expert. Moreover, the fuzzy inference process refers the knowledge base produced by INFD and fuzzy rule base that can improve the weakness of conventional filters in heavily corrupted condition. The intelligent FIF achieves better performance than the other filters based on the criteria of Mean Absolute Error (MAE), and Mean Square Error (MSE). By the experiments, FIF still keeps the high performance to filtering impulse noise from color image.

AB - This paper proposes an intelligent Fuzzy Image Filter (FIF) to remove impulse noise. The filter including two processes, the Intelligent Fuzzy Number Deciding (IFND) process and fuzzy inference process, to filter impulse noise from heavily corrupted images efficiently. IFN D can automatically decide the number of fuzzy number based on image features to overcome the drawbacks of Adaptive Weighted Fuzzy Mean (AWFM) filter that must be defined by domain expert. Moreover, the fuzzy inference process refers the knowledge base produced by INFD and fuzzy rule base that can improve the weakness of conventional filters in heavily corrupted condition. The intelligent FIF achieves better performance than the other filters based on the criteria of Mean Absolute Error (MAE), and Mean Square Error (MSE). By the experiments, FIF still keeps the high performance to filtering impulse noise from color image.

UR - http://www.scopus.com/inward/record.url?scp=0036454150&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036454150&partnerID=8YFLogxK

M3 - Article

VL - 1

SP - 431

EP - 436

JO - IEEE International Conference on Fuzzy Systems

JF - IEEE International Conference on Fuzzy Systems

SN - 1098-7584

ER -