An algorithm for improving Non-Local Means operators via low-rank approximation

TL;DR

This paper introduces a low-rank approximation technique for Non-Local Means operators, enhancing noise reduction in image denoising by applying spectral filtering and Chebyshev polynomial implementation.

Contribution

The paper proposes a novel low-rank spectral filtering method to improve Non-Local Means operators, with efficient Chebyshev polynomial implementation for natural image denoising.

Findings

Enhanced noise robustness in denoising results

Efficient implementation using Chebyshev polynomials

Competitive performance compared to leading methods

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.