We present an in-depth analysis of a variation of the Non-local Means (NLM) image denoising algorithm that uses principal component analysis (PCA) to achieve a higher accuracy while reducing computational load. Image neighborhood vectors are first projected onto a lower-dimensional subspace using PCA. The dimensionality of this subspace is chosen automatically using parallel analysis. Consequently, neighborhood similarity weights for denoising are computed using distances in this subspace

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... than the full space. The resulting algorithm is referred to as Principal Neighborhood Dictionary (PND) Nonlocal Means. We investigate PND'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. The accuracy of NLM and PND are also examined with respect to the choice of image neighborhood and search window sizes. Finally, we present a quantitative and qualitative comparison of PND vs. NLM and another image neighborhood PCA-based state-of-the-art image denoising algorithm. Index Terms-Principal neighborhood, non-local means, principal component analysis, image denoising, parallel analysis.