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

  • Authors:

  • Lei Zhang;Weisheng Dong;David Zhang;Guangming Shi

  • Affiliations:

  • Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China and Key Laboratory of Intelligent Perception and Image Understanding (Chinese Ministry of Education), School of Elec ...;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Key Laboratory of Intelligent Perception and Image Understanding (Chinese Ministry of Education), School of Electronic Engineering, Xidian University, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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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.