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An Efficient Method to Compute a Covariance Matrix of the Non-local Means Algorithm for Image Denoising with the Principal Component Analysis
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  • Journal title : Journal of Broadcast Engineering
  • Volume 21, Issue 1,  2016, pp.60-65
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2016.21.1.60
 Title & Authors
An Efficient Method to Compute a Covariance Matrix of the Non-local Means Algorithm for Image Denoising with the Principal Component Analysis
Kim, Jeonghwan; Jeong, Jechang;
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 Abstract
This paper introduces the non-local means (NLM) algorithm for image denoising, and also introduces an improved algorithm which is based on the principal component analysis (PCA). To do the PCA, a covariance matrix of a given image should be evaluated first. If we let the size of neighborhood patches of the NLM S × S2, and let the number of pixels Q, a matrix multiplication of the size S2 × Q is required to compute a covariance matrix. According to the characteristic of images, such computation is inefficient. Therefore, this paper proposes an efficient method to compute the covariance matrix by sampling the pixels. After sampling, the covariance matrix can be computed with matrices of the size S2 × floor (Width/l) × (Height/l).
 Keywords
processing;denoising;non-local means;principal components analysis;covariance matrix;
 Language
Korean
 Cited by
 References
1.
A. Buades, B. Coll, and J.-M. Morel, "A non-local algorithm for image denoising," CVPR, vol. 2, pp. 60-65, June, 2005.

2.
T. Tasdizen, “Principal neighborhood dictionaries for nonlocal means image denoising,” IEEE Trans., Image processing, vol. 18, no. 12, pp. 2649-2660, Sep, 2009. crossref(new window)

3.
J. Salmon, “On two parameters for denoising with non-local means,” IEEE Signal processing letters, vol. 17, no. 3, pp. 269-272, Jan, 2010. crossref(new window)

4.
L. I Smith, "A tutorial on principal components analysis," Feb, 2002, http://www.cs.otago.ac.nz/cosc453.

5.
C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," ICCV, pp. 839-846, Jan, 1998.

6.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans., Image processing, vol. 16, no. 8, pp. 2080-2095, Aug, 2007. crossref(new window)

7.
S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image processing, vol. 9, no. 9, pp. 1532-1546, Sep, 2000. crossref(new window)