Laplacian-preprocessed impulse-noise detection, with image denoising via difference-mean-filtering of long-range-correlated sub-images

Javad Ahmadi-Shokouh, Kainam Thomas Wong, Edmund Hui On Ng

Research output: Journal article publicationConference articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Zhang & Karim's Laplacian-preprocessed detector [15] is robust against mis-identification of an image's thin-lines as impulse-noise-corrupted pixels. Wang & Zhang's "long-range correlation" denoising scheme [9] exploits any information-redundancy between an identified corrupted-pixel's local neighborhood with distant sub-images, to restore the corrupted pixel. This paper synergizes the above two algorithms, with the following algorithmic enhancements: (1) A pre-tuning of Zhang & Karim's threshold based on a rough estimation of the corrupting impulse-noise's spatial probability of occurrence, assuming the availability of a test-image "sufficiently" similar to the given corrupted image. (2) A new "difference-mean" criterion for better pixel-restoration. Limited simulations illustrate the above proposed scheme's efficacy and improvements.
Original languageEnglish
Pages (from-to)1569-1572
Number of pages4
JournalConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2
Publication statusPublished - 1 Dec 2004
Externally publishedYes
EventConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 7 Nov 200410 Nov 2004

Keywords

  • Image matching
  • Image processing
  • Image restoration
  • Impulse noise
  • Median filters
  • Nonlinear detection
  • Shot noise
  • Switched filters

ASJC Scopus subject areas

  • Engineering(all)

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