Adaptive direction-guided structure tensor total variation


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Demircan-Tureyen E., Kamaşak M. E.

SIGNAL PROCESSING-IMAGE COMMUNICATION, vol.99, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 99
  • Publication Date: 2021
  • Doi Number: 10.1016/j.image.2021.116497
  • Journal Name: SIGNAL PROCESSING-IMAGE COMMUNICATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Istanbul Kültür University Affiliated: Yes

Abstract

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.