Adaptive direction-guided structure tensor total variation

Demircan-Tureyen E., Kamaşak M. E.

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

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 99
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.image.2021.116497
  • Dergi Adı: SIGNAL PROCESSING-IMAGE COMMUNICATION
  • Derginin Tarandığı İndeksler: 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
  • Anahtar Kelimeler: Variational models, Image denoising, Directional total variation, Structure tensor, Orientation field estimation, Inverse problems, NOISE REMOVAL, ALGORITHM, MINIMIZATION, IMAGES, MODEL
  • İstanbul Teknik Üniversitesi Adresli: Evet

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.