An automatic filtering convergence method for iterative impulse noise filters based on PSNR checking and filtered pixels detection

Chao Yu Chen, Chin-Hsing Chen, Chao Ho Chen, Kuo Ping Lin

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Whether input images are corrupted by impulse noise and what the noise density level is are unknown a priori, and thus published iterative impulse noise filters cannot adaptively reduce noise, resulting in a smoothing image or unclear de-noising. For this reason, this paper proposes an automatic filtering convergence method using PSNR checking and filtered pixel detection for iterative impulse noise filters. (1) First, the similarity between the input image and the 1st filtered image is determined by calculating MSE. If MSE is equal to 0, then the input image is unfiltered and becomes the output. (2) Otherwise, one applies PSNR checking and filtered pixel detection to estimate the difference between the tth filtered image and the t–1th filtered image. (3) Finally, an adaptive and reasonable threshold is defined to make the iterative impulse noise filters stop automatically for most image details preservation in finite steps. Experimental results show that iterative impulse noise filters with the proposed automatic filtering convergence method can remove much of the impulse noise and effectively maintain image details. In addition, iterative impulse noise filters operate more efficiently.

Original languageEnglish
Pages (from-to)198-207
Number of pages10
JournalExpert Systems With Applications
Volume63
DOIs
Publication statusPublished - 2016 Nov 30

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'An automatic filtering convergence method for iterative impulse noise filters based on PSNR checking and filtered pixels detection'. Together they form a unique fingerprint.

  • Cite this