An algorithm for improving Non-Local Means operators via low-rank approximation
Computer Vision and Pattern Recognition
2014-12-08 v1 General Mathematics
Abstract
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.
Cite
@article{arxiv.1412.2067,
title = {An algorithm for improving Non-Local Means operators via low-rank approximation},
author = {Victor May and Yosi Keller and Nir Sharon and Yoel Shkolnisky},
journal= {arXiv preprint arXiv:1412.2067},
year = {2014}
}