Unified Models for Second-Order TV-Type Regularisation in Imaging: A New Perspective Based on Vector Operators

Eva Maria Brinkmann*, Martin Burger, Joana Sarah Grah

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

We introduce a novel regulariser based on the natural vector field operations gradient, divergence, curl and shear. For suitable choices of the weighting parameters contained in our model, it generalises well-known first- and second-order TV-type regularisation methods including TV, ICTV and TGV2 and enables interpolation between them. To better understand the influence of each parameter, we characterise the nullspaces of the respective regularisation functionals. Analysing the continuous model, we conclude that it is not sufficient to combine penalisation of the divergence and the curl to achieve high-quality results, but interestingly it seems crucial that the penalty functional includes at least one component of the shear or suitable boundary conditions. We investigate which requirements regarding the choice of weighting parameters yield a rotational invariant approach. To guarantee physically meaningful reconstructions, implying that conservation laws for vectorial differential operators remain valid, we need a careful discretisation that we therefore discuss in detail.

Original languageEnglish
JournalJournal of Mathematical Imaging and Vision
DOIs
Publication statusE-pub ahead of print - 16 Nov 2018

Keywords

  • (Higher-order) total variation (TV) regularisation
  • Denoising
  • Helmholtz decomposition
  • Natural differential operators
  • Sparse regularisation
  • Variational methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Condensed Matter Physics
  • Computer Vision and Pattern Recognition
  • Geometry and Topology
  • Applied Mathematics

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