An algorithm for improving Non-Local Means operators via low-rank approximation
This is an incremental improvement for image denoising applications.
The paper tackles the problem of noise sensitivity in Non-Local Means operators by computing a low-rank approximation via spectral filtering, resulting in an operator that preserves key properties while reducing noise, with demonstrations on natural image denoising and comparisons to leading methods.
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.