A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision

Sheng Fu Liang, Shih-Mao Lu, Jyh-Yeong Chang, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

In this paper, a novel two-stage noise removal algorithm to deal with impulse noise is proposed. In the first stage, an adaptive two-level feedforward neural network (NN) with a back-propagation training algorithm was applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by the human visual system (HVS) are proposed to classify the image pixels into human perception sensitive class and nonsensitive class, and to compensate the blur of the edge and the destruction caused by the median filter. An NN is proposed to enhance the sensitive regions with higher visual quality. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and smoothness in edge regions.
Original languageEnglish
Pages (from-to)863-873
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume16
Issue number4
DOIs
StatePublished - Aug 2008

Keywords

  • Fuzzy decision system
  • human visual system (HVS)
  • impulse noise
  • Neural network (NN)
  • noise removal

Fingerprint Dive into the research topics of 'A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision'. Together they form a unique fingerprint.

Cite this