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;
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).