title: PRINCIPAL COMPONENTS FOR NON-LOCAL MEANS IMAGE DENOISING.
abstract: This paper presents an image denoising algorithm that uses principal component analysis (PCA) in conjunction with the non-local means image denoising. Image neighborhood vectors used in the non-local means algorithm are first projected onto a lower-dimensional subspace using PCA. Consequently, neighborhood similarity weights for denoising are computed using distances in this subspace rather than the full space. This modification to the non-local means algorithm results in improved accuracy and computational performance. We present an analysis of the proposed method's accuracy as a function of the dimensionality of the projection subspace and demonstrate that denoising accuracy peaks at a relatively low number of dimensions.
authors.affiliation: Electrical and Computer Engineering Department, University of Utah.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen