A salt & pepper noise filter based on local and global image information

Zuoyong Li, Yong Cheng, Kezong Tang, Yong Xu, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)

Abstract

Existing salt & pepper noise filters only use local image information to detect noise pixels, and neglect global image information. This makes them inapplicable to images with noise-free pixel blocks composed of uncorrupted pixels of gray level extremes, either 0 or 255. In addition, existing filters are hard to simultaneously obtain low miss detection (MD) and low false alarm (FA) in noise detection. To alleviate these issues, we proposed an innovative noise filter based on local and global image information. The proposed filter developed an image block-based method to more accurately estimate noise density of an image, and presented a global image information-based noise detection rectification method. The noise density estimation result was used in subsequent noise detection and rectification stages. Furthermore, the proposed filter combined and slightly revised noise detection schemes of two existing switching filters to improve the accuracy of noise detection. Experimental results on a series of images showed that the proposed filter achieved significant improvement, especially on images with noise-free pixel blocks of gray level extremes.
Original languageEnglish
Pages (from-to)172-185
Number of pages14
JournalNeurocomputing
Volume159
Issue number1
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Global information
  • Image denoising
  • Image thresholding
  • Local information
  • Salt & pepper noise

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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