An Algorithm for Improving Non-Local Means Operators via Low-Rank Approximation

Victor May, Yosi Keller, Nir Sharon, Yoel Shkolnisky

Research output: Contribution to journalArticlepeer-review

Abstract

We present a method for improving a non-local means (NLM) operator by computing its low-rank approximation. The low-rank operator is constructed by applying a filter to the spectrum of the original NLM 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 comparison of our method with other denoising methods.

Original languageEnglish
Article number7384488
Pages (from-to)1340-1353
Number of pages14
JournalIEEE Transactions on Image Processing
Volume25
Issue number3
DOIs
StatePublished - Mar 2016

Keywords

  • Chebyshev polynomials
  • Denoising
  • Non-Local Means operator

Fingerprint

Dive into the research topics of 'An Algorithm for Improving Non-Local Means Operators via Low-Rank Approximation'. Together they form a unique fingerprint.

Cite this