  We present a method for improving a Non Local Means operator by computing its
low-rank approximation. The low-rank operator is constructed by applying a
filter to the spectrum of the original Non Local Means operator. This results
in an operator which is less sensitive to noise while preserving important
properties of the original operator. The method is efficiently implemented
based on Chebyshev polynomials and is demonstrated on the application of
natural images denoising. For this application, we provide a comprehensive
comparison of our method with leading denoising methods.
