Nonlinear Spectral Analysis via One-Homogeneous Functionals: Overview and Future Prospects

Guy Gilboa, Michael Moeller, Martin Burger

Research output: Contribution to journalArticlepeer-review


We present in this paper the motivation and theory of nonlinear spectral representations, based on convex regularizing functionals. Some comparisons and analogies are drawn to the fields of signal processing, harmonic analysis, and sparse representations. The basic approach, main results, and initial applications are shown. A discussion of open problems and future directions concludes this work.

Original languageEnglish
Pages (from-to)300-319
Number of pages20
JournalJournal of Mathematical Imaging and Vision
Issue number2
StatePublished - 1 Oct 2016


  • Image decomposition
  • Nonlinear eigenvalue problem
  • Nonlinear spectral representations
  • One-homogeneous functionals
  • Total variation

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Nonlinear Spectral Analysis via One-Homogeneous Functionals: Overview and Future Prospects'. Together they form a unique fingerprint.

Cite this