Optimal representation of piecewise Hölder smooth bivariate functions by the Easy Path Wavelet Transform

Link:
Autor/in:
Erscheinungsjahr:
2013
Medientyp:
Text
Schlagworte:
  • Image denoising
  • Wavelet transforms
  • Curvelet transform
  • Algorithms
  • Computer Vision
  • Models
  • Image denoising
  • Wavelet transforms
  • Curvelet transform
  • Algorithms
  • Computer Vision
  • Models
Beschreibung:
  • The Easy Path Wavelet Transform (EPWT) (Plonka, 2009) [26] has recently been proposed by one of the authors as a tool for sparse representations of bivariate functions from discrete data, in particular from image data. The EPWT is a locally adaptive wavelet transform. It works along pathways through the array of function values and it exploits the local correlations of the given data in a simple appropriate manner. In this paper, we aim to provide a theoretical understanding of the performance of the EPWT. In particular, we derive conditions for the path vectors of the EPWT that need to be met in order to achieve optimal N-term approximations for piecewise Hölder smooth functions with singularities along curves. © 2013 Elsevier Inc.
Lizenz:
  • info:eu-repo/semantics/openAccess
Quellsystem:
Forschungsinformationssystem der UHH
Interne Metadaten
Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/2aecc249-12f1-4930-96e6-da9b29f60d4f