This paper introduces an extremely robust adaptive

denoising filter in the spatial domain. The filter is based on

non-parametric statistical estimation methods, and in particular

generalizes an adaptive method proposed earlier by Fukunaga

[1]. To denoise a pixel, the proposed filter computes a locally

adaptive set of weights and window sizes, which can be proven

to be optimal in the context of non-parametric estimation using

kernels. While we do not report analytical results on the statistical

efficiency of the proposed method in this paper, we will discuss

its derivation, and experimentally demonstrate its effectiveness

against competing techniques at low SNR and on real noisy data.