• Medientyp: E-Book; Video
  • Titel: Regularization of Inverse Problems via Metamorphosis along Geodesics in Image Spaces
  • Beteiligte: Steidl, Gabriele [Verfasser:in]; Beretta, Elena (Organisation) [Sonstige Person, Familie und Körperschaft]; Ascher, Uri (Organisation) [Sonstige Person, Familie und Körperschaft]; Scherzer, Otmar (Organisation) [Sonstige Person, Familie und Körperschaft]; Vese, Luminita (Organisation) [Sonstige Person, Familie und Körperschaft]; Neumayer, Sebastian [Sonstige Person, Familie und Körperschaft]; Persch, Johannes [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [Erscheinungsort nicht ermittelbar]: Banff International Research Station (BIRS) for Mathematical Innovation and Discovery, 2019
  • Erschienen in: Reconstruction Methods for Inverse Problems (19w5092) ; (Jan. 2019)
  • Umfang: 1 Online-Ressource (119 MB, 00:43:37:13)
  • Sprache: Englisch
  • DOI: 10.5446/56558
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Audiovisuelles Material
  • Beschreibung: This talk addresses the solution of inverse problems in imaging given an additional reference image. We combine a modification of the discrete geodesic path model of Berkels, Effland and Rumpf with a variational model, actually the L 2 -T V model, for image restoration. We prove that the space continuous model has a minimizer and propose a minimization procedure which alternates over the involved sequences of deformations and images. The minimization with respect to the image sequence exploits recent algorithms from convex analysis to minimize the L 2 -T V functional. For the numerical computation we apply a finite difference approach on staggered grids together with a multilevel strategy. We present proof-of-the-concept numerical results for sparse and limited angle computerized tomography as well as for superresolution demonstrating the power of the method. Further we apply the morphing approach for image colorization
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung - Nicht kommerziell (CC BY-NC)