On the Direction Guidance in Structure Tensor Total Variation Based Denoising

Ezgi Demircan-Tureyen; Mustafa E. Kamasak

doi:10.1007/978-3-030-31332-6_8

On the Direction Guidance in Structure Tensor Total Variation Based Denoising

Ezgi Demircan-Tureyen^*
, Mustafa E. Kamasak

^*Corresponding author for this work

Department of Computer Engineering

Istanbul Kultur University
Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings
Editors	Aythami Morales, Julian Fierrez, José Salvador Sánchez, Bernardete Ribeiro
Publisher	Springer
Pages	89-100
Number of pages	12
ISBN (Print)	9783030313319
DOIs	https://doi.org/10.1007/978-3-030-31332-6_8
Publication status	Published - 2019
Event	9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019 - Madrid, Spain Duration: 1 Jul 2019 → 4 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11867 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019
Country/Territory	Spain
City	Madrid
Period	1/07/19 → 4/07/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Funding

This work is supported by Istanbul Kultur University under ULEP-2018-2019/49.

Funders	Funder number
Istanbul Kultur University	ULEP-2018-2019/49

Keywords

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

Access to Document

10.1007/978-3-030-31332-6_8

Cite this

Demircan-Tureyen, E., & Kamasak, M. E. (2019). On the Direction Guidance in Structure Tensor Total Variation Based Denoising. In A. Morales, J. Fierrez, J. S. Sánchez, & B. Ribeiro (Eds.), Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings (pp. 89-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11867 LNCS). Springer. https://doi.org/10.1007/978-3-030-31332-6_8

Demircan-Tureyen, Ezgi ; Kamasak, Mustafa E. / On the Direction Guidance in Structure Tensor Total Variation Based Denoising. Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings. editor / Aythami Morales ; Julian Fierrez ; José Salvador Sánchez ; Bernardete Ribeiro. Springer, 2019. pp. 89-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Convex optimization, Directional total variation, Image denoising, Inverse problems, Structure tensor, Total variation",

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Demircan-Tureyen, E & Kamasak, ME 2019, On the Direction Guidance in Structure Tensor Total Variation Based Denoising. in A Morales, J Fierrez, JS Sánchez & B Ribeiro (eds), Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11867 LNCS, Springer, pp. 89-100, 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, Madrid, Spain, 1/07/19. https://doi.org/10.1007/978-3-030-31332-6_8

On the Direction Guidance in Structure Tensor Total Variation Based Denoising. / Demircan-Tureyen, Ezgi; Kamasak, Mustafa E.
Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings. ed. / Aythami Morales; Julian Fierrez; José Salvador Sánchez; Bernardete Ribeiro. Springer, 2019. p. 89-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11867 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On the Direction Guidance in Structure Tensor Total Variation Based Denoising

AU - Demircan-Tureyen, Ezgi

AU - Kamasak, Mustafa E.

PY - 2019

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N2 - 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.

AB - 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.

KW - Convex optimization

KW - Directional total variation

KW - Image denoising

KW - Inverse problems

KW - Structure tensor

KW - Total variation

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DO - 10.1007/978-3-030-31332-6_8

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings

A2 - Morales, Aythami

A2 - Fierrez, Julian

A2 - Sánchez, José Salvador

A2 - Ribeiro, Bernardete

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ER -

Demircan-Tureyen E, Kamasak ME. On the Direction Guidance in Structure Tensor Total Variation Based Denoising. In Morales A, Fierrez J, Sánchez JS, Ribeiro B, editors, Pattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings. Springer. 2019. p. 89-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-31332-6_8

On the Direction Guidance in Structure Tensor Total Variation Based Denoising

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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