On the Direction Guidance in Structure Tensor Total Variation Based Denoising

Ezgi Demircan-Tureyen*, Mustafa E. Kamasak

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

4 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıPattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings
EditörlerAythami Morales, Julian Fierrez, José Salvador Sánchez, Bernardete Ribeiro
YayınlayanSpringer
Sayfalar89-100
Sayfa sayısı12
ISBN (Basılı)9783030313319
DOI'lar
Yayın durumuYayınlandı - 2019
Etkinlik9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019 - Madrid, Spain
Süre: 1 Tem 20194 Tem 2019

Yayın serisi

AdıLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim11867 LNCS
ISSN (Basılı)0302-9743
ISSN (Elektronik)1611-3349

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???event.eventtypes.event.conference???9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019
Ülke/BölgeSpain
ŞehirMadrid
Periyot1/07/194/07/19

Bibliyografik not

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Finansman

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

FinansörlerFinansör numarası
Istanbul Kultur UniversityULEP-2018-2019/49

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