Weighted fuzzy mean filters for image processing

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

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)


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
Issue number2
Publication statusPublished - 1997 Jan 1

All Science Journal Classification (ASJC) codes

  • Logic
  • Artificial Intelligence


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