An Efficient Method to Compute a Covariance Matrix of the Non-local Means Algorithm for Image Denoising with the Principal Component Analysis

- 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;

Kim, Jeonghwan; Jeong, Jechang;

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 × S^{2}, and let the number of pixels Q, a matrix multiplication of the size S^{2} × 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 S^{2} × floor (Width/l) × (Height/l).

Keywords

processing;denoising;non-local means;principal components analysis;covariance matrix;

Language

Korean

References

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