Intelligent adaptive subband-based multi-state median filter in lowly-corrupted images

Chao Ho Chen, Chao Yu Chen, Chin Hsing Chen

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This paper presents a novel method to reduce random-valued impulse noise by adaptive subband-based multi-state median (ASBMSM) filtering for lowly-corrupted images. Most reported noise filters can effectively reduce impulse noises that are distributed over the low-frequency area but hardly filter those noises that are distributed over the high-frequency area in an image. To overcome the above problem, the paper develops an adaptive subband-based filtering scheme, in which an image is divided into low-frequency and high-frequency blocks of size 8*8 by using PSNR (peak signal-to-noise-ratio) checking. Then, different blocks use different masks for filtering. In addition, to enhance restoration of the original information, another filtering process is required if the PSNR value of the previously filtered image is lower than a threshold. Experimental results manifest that the proposed impulse noise filter is superior in PSNR performance to other switching-based median filters when the corruption ratio is below 30%.

Original languageEnglish
Pages (from-to)2917-2926
Number of pages10
JournalInternational Journal of Innovative Computing, Information and Control
Volume5
Issue number9
Publication statusPublished - 2009 Sep 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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