Nonlocal adaptive direction-guided structure tensor total variation for image recovery

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Demircan-Tureyen E., Kamaşak M. E.

Signal, Image and Video Processing, vol.15, no.7, pp.1517-1525, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 7
  • Publication Date: 2021
  • Doi Number: 10.1007/s11760-021-01884-8
  • Journal Name: Signal, Image and Video Processing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
  • Page Numbers: pp.1517-1525
  • Keywords: Directional total variation, Image recovery, Nonlocal regularization, Orientation field estimation, Structure tensor
  • Istanbul Kültür University Affiliated: Yes

Abstract

A common strategy in variational image recovery is utilizing the nonlocal self-similarity property, when designing energy functionals. One such contribution is nonlocal structure tensor total variation (NLSTV), which lies at the core of this study. This paper is concerned with boosting the NLSTV regularization term through the use of directional priors. More specifically, NLSTV is leveraged so that, at each image point, it gains more sensitivity in the direction that is presumed to have the minimum local variation. The actual difficulty here is capturing this directional information from the corrupted image. In this regard, we propose a method that employs anisotropic Gaussian kernels to estimate directional features to be later used by our proposed model. The experiments validate that our entire two-stage framework achieves better results than the NLSTV model and two other competing local models, in terms of visual and quantitative evaluation.