Two-stage image denoising by principal component analysis with local pixel grouping

Lei Zhang, Weisheng Dong, Dapeng Zhang, Guangming Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

499 Citations (Scopus)

Abstract

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.
Original languageEnglish
Pages (from-to)1531-1549
Number of pages19
JournalPattern Recognition
Volume43
Issue number4
DOIs
Publication statusPublished - 1 Apr 2010

Keywords

  • Denoising
  • Edge preservation
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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