Two Phase Image Denoising By Principal Component Analysis and Local Pixel Grouping

Authors(1) :-Nain Yadav

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.

Authors and Affiliations

Nain Yadav
Department of Computer Science, Naraina Group of Institutions, Kanpur, Uttar Pradesh, India

Denoising, Principal component analysis, Edge preservation

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 844-846
Manuscript Number : CSEIT1723204
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Nain Yadav, "Two Phase Image Denoising By Principal Component Analysis and Local Pixel Grouping ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.844-846, May-June-2017.
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