On the Direction Guidance in Structure Tensor Total Variation Based Denoising

Ezgi Demircan-Tureyen*, Mustafa E. Kamasak

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

This paper introduces a new analysis-based regularizer, which incorporates the neighborhood-awareness of the structure tensor total variation (STV) and the tunability of the directional total variation (DTV), in favor of a pre-selected direction with a pre-selected dose of penalization. In order to show the utility of the proposed regularizer, we consider the problem of denoising uni-directional images. Since the regularizer is convex, we develop a simple optimization algorithm by realizing its proximal map. The quantitative and the visual experiments demonstrate the superiority of our regularizer over DTV (only for scalar-valued images) and STV.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings
EditorsAythami Morales, Julian Fierrez, José Salvador Sánchez, Bernardete Ribeiro
PublisherSpringer
Pages89-100
Number of pages12
ISBN (Print)9783030313319
DOIs
Publication statusPublished - 2019
Event9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019 - Madrid, Spain
Duration: 1 Jul 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11867 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019
Country/TerritorySpain
CityMadrid
Period1/07/194/07/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

  • Convex optimization
  • Directional total variation
  • Image denoising
  • Inverse problems
  • Structure tensor
  • Total variation

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