A HVS-directed neural-network-based approach for salt-pepper impulse noise removal

Shih Mao Lu, Sheng-Fu Liang, Chin Teng Lin

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, a novel two-stage noise removal algorithm to deal with salt-pepper impulse noise is proposed. In the first stage, the decision-based recursive adaptive noise-exclusive median filter is applied to remove the noise cleanly and to keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify image pixels into human perception sensitive class and non-sensitive class. A neural network is proposed to compensate the sensitive regions for image quality enhancement. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions of the resultant images.

Original languageEnglish
Pages (from-to)925-939
Number of pages15
JournalJournal of Information Science and Engineering
Volume22
Issue number4
Publication statusPublished - 2006 Jul

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Library and Information Sciences

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