An accurate operator splitting scheme for nonlinear diffusion filtering

Danny Barash, Moshe Israeli, Ron Kimmel

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

15 Scopus citations

Abstract

Efficient numerical schemes for nonlinear diffusion filtering based on additive operator splitting (AOS) were introduced in [10]. AOS schemes are efficient and unconditionally stable, yet their accuracy is low. Future applications of nonlinear diffusion filtering may require additional accuracy at the expense of a relatively modest cost in computations and complexity. To investigate the effect of higher accuracy schemes, we first examine the Crank-Nicolson and DuFort-Frankel second-order schemes in one dimension. We then extend the AOS schemes to take advantage of the higher accuracy that is achieved in one dimension, by using symmetric multiplicative splittings. Quantitative comparisons are performed for small and large time steps, as well as visual examination of images to find out whether the improvement in accuracy is noticeable.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Scale-Space and Morphology in Computer Vision, Scale-Space 2001
Pages281-289
Number of pages9
StatePublished - 1 Jan 2001
Externally publishedYes
Event3rd International Conference on Scale-Space and Morphology in Computer Vision, Scale-Space 2001 - Vancouver, Canada
Duration: 7 Jul 20018 Jul 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
ISSN (Print)0302-9743

Conference

Conference3rd International Conference on Scale-Space and Morphology in Computer Vision, Scale-Space 2001
Country/TerritoryCanada
CityVancouver
Period7/07/018/07/01

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