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.
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