Structural Similarity-Based Nonlocal Variational Models for Image Restoration

Wei Wang*, Fang Li, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

28 Citations (Scopus)


In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models.

Original languageEnglish
Article number8672180
Pages (from-to)4260-4272
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number9
Publication statusPublished - Sept 2019

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • gradient
  • Image restoration
  • nonlocal variational model
  • regularization
  • structural similarity index


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