A novel fuzzy filter for impulse noise removal

Chang Shing Lee, Shu-Mei Guo, Chin Yuan Hsu

Research output: Contribution to journalArticlepeer-review

13 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
Pages (from-to)375-380
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3174
Publication statusPublished - 2004

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

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