Adaptive direction-guided structure tensor total variation

Ezgi Demircan-Tureyen*, Mustafa E. Kamasak

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1 Atıf (Scopus)

Özet

Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.

Orijinal dilİngilizce
Makale numarası116497
DergiSignal Processing: Image Communication
Hacim99
DOI'lar
Yayın durumuYayınlandı - Kas 2021

Bibliyografik not

Publisher Copyright:
© 2021 Elsevier B.V.

Finansman

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under 115R285 .

FinansörlerFinansör numarası
TUBITAK115R285
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

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