Weighted fuzzy mean filters for image processing

Chang Shing Lee, Yau-Hwang Kuo, Pao Ta Yu

Research output: Contribution to journalArticle

119 Citations (Scopus)

Abstract

A new fuzzy filter for the removal of heavy additive impulse noise, called the weighted fuzzy mean (WFM) filter, is proposed and analyzed in this paper. The WFM-filtered output signal is the mean value of the corrupted signals in a sample matrix, and these signals are weighted by a membership grade of an associated fuzzy set stored in a knowledge base. The knowledge base contains a number of fuzzy sets decided by experts or derived from the histogram of a reference image. When noise probability exceeds 0.3, WFM gives very superior performance compared with conventional filters when evaluated by mean absolute error (MAE), mean square error (MSE), peak signal-to-noise-rate (PSNR) and subjective evaluation criteria. For dedicated hardware implementation, WFM is also much simpler than the conventional median filter.

Original languageEnglish
Pages (from-to)157-180
Number of pages24
JournalFuzzy Sets and Systems
Volume89
Issue number2
DOIs
Publication statusPublished - 1997 Jan 1

Fingerprint

Fuzzy sets
Image Processing
Image processing
Fuzzy filters
Filter
Median filters
Impulse noise
Mean square error
Knowledge Base
Fuzzy Sets
Hardware
Fuzzy Filter
Impulse Noise
Median Filter
Subjective Evaluation
Hardware Implementation
Mean Value
Histogram
Exceed
Output

All Science Journal Classification (ASJC) codes

  • Logic
  • Artificial Intelligence

Cite this

Lee, Chang Shing ; Kuo, Yau-Hwang ; Yu, Pao Ta. / Weighted fuzzy mean filters for image processing. In: Fuzzy Sets and Systems. 1997 ; Vol. 89, No. 2. pp. 157-180.
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Weighted fuzzy mean filters for image processing. / Lee, Chang Shing; Kuo, Yau-Hwang; Yu, Pao Ta.

In: Fuzzy Sets and Systems, Vol. 89, No. 2, 01.01.1997, p. 157-180.

Research output: Contribution to journalArticle

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