A robust neuro-fuzzy network approach to impulse noise filtering for color images

Yueyang Li, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, an effective impulse noise filter for color images is presented. It consists of two modes of operation, namely, training and testing (filtering). During training, the impulse detector is used to locate the noisy pixels in the color images for optimizing the RNF network. During testing, if a pixel is detected as a corrupted one according to the impulse detector, the trained RNF network will be triggered to output a new pixel to replace it. The proposed impulse noise filter is distinguished by two properties. The first is the use of a simple impulse detector, which is efficient and yet effective in detecting the noisy pixels in color images. The other is the use of a novel membership function in the design of the adaptive RNF network, making the network robust to impulse noise. As demonstrated by the experimental results, the proposed filter not only has the abilities of noise attenuation and details preservation but also possesses desirable robustness and adaptive capabilities. It outperforms other conventional multichannel filters.
Original languageEnglish
Pages (from-to)872-884
Number of pages13
JournalApplied Soft Computing Journal
Issue number2
Publication statusPublished - 1 Mar 2008


  • Color image processing
  • Impulse noise
  • Multichannel filter
  • Neuro-fuzzy network

ASJC Scopus subject areas

  • Software

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