Iterated nonlocal means for texture restoration

Thomas Brox, Daniel Cremers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

50 Scopus citations

Abstract

The recent nonlocal means filter is a very successful technique for denoising textured images. In this paper, we formulate a variational technique that leads to an adaptive version of this filter. In particular, in an iterative manner, the filtering result is employed to redefine the similarity of patches in the next iteration. We further introduce the idea to replace the neighborhood weighting by a sorting criterion. This addresses the parameter selection problem of the original nonlocal means filter and leads to favorable denoising results of textured images, particularly in case of large noise levels.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision, First International Conference, SSVM 2007, Proceedings
PublisherSpringer Verlag
Pages13-24
Number of pages12
ISBN (Print)9783540728221
DOIs
StatePublished - 2007
Externally publishedYes
Event1st International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2007 - Ischia, Italy
Duration: 30 May 20072 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4485 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2007
Country/TerritoryItaly
CityIschia
Period30/05/072/06/07

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