Nonlocal means with dimensionality reduction and SURE-based parameter selection.

Abstract

Nonlocal means (NLM) is an effective denoising method that applies adaptive averaging based on similarity between neighborhoods in the image. An attractive way to both improve and speed-up NLM is by first performing a linear projection of the neighborhood. One particular example is to use principal components analysis (PCA) to perform dimensionality reduction. Here, we derive Stein's unbiased risk estimate (SURE) for NLM with linear projection of the neighborhoods. The SURE can then be used to optimize the parameters by a search algorithm or we can consider a linear expansion of multiple NLMs, each with a fixed parameter set, for which the optimal weights can be found by solving a linear system of equations. The experimental results demonstrate the accuracy of the SURE and its successful application to tune the parameters for NLM.