Two-stage Image Denoising by Principal Component Analysis with Local Pixel Grouping

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Authors Lei Zhang, Weisheng Dong, David Zhang, Guangming Shi
Journal/Conference Name Pattern Recognition
Paper Category
Paper 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
Date of publication 2010
Code Programming Language MATLAB
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