  To denoise a reference patch, the Non-Local-Means denoising filter processes
a set of neighbor patches. Few Nearest Neighbors (NN) are used to limit the
computational burden of the algorithm. Here here we show analytically that the
NN approach introduces a bias in the denoised patch, and we propose a different
neighbors' collection criterion, named Statistical NN (SNN), to alleviate this
issue. Our approach outperforms the traditional one in case of both white and
colored noise: fewer SNNs generate images of higher quality, at a lower
computational cost.
