A novel fuzzy filter for impulse noise removal

Chang Shing Lee, Shu Mei Guo, Chin Yuan Hsu

Research output: Chapter in Book/Report/Conference proceedingChapter

18 Citations (Scopus)

Abstract

In this paper, we propose a novel Neural Fuzzy Filter (NFF) to remove impulse noise from highly corrupted images. The proposed filter consists of a fuzzy number construction process, a neural fuzzy filtering process and an image knowledge base. First, the fuzzy number construction process will receive sample images or the noise-free image, then construct an image knowledge base for the neural fuzzy filtering process. Second, the neural fuzzy filtering process contains of a neural fuzzy mechanism, & fuzzy mean process, and a fuzzy decision process to perform the task of impulse noise removing. By the experimental results, NFF achieves better performance than the state-of-theart filters based on the criteria of Mean-Square-Error (MSB). On the subjective evaluation of those filtered images, NFF also results in a higher quality of global restoration.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsFuliang Yin, Chengan Guo, Jun Wang
PublisherSpringer Verlag
Pages375-380
Number of pages6
ISBN (Print)3540228438, 9783540228431
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3174
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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