A general sparse image prior combination in super-resolution

Salvador Villena, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper the Super-Resolution (SR) image registration and reconstruction problem is studied within the Bayesian framework using a general sparse image prior combination. The representation of the proposed priors as Scale Mixtures of Gaussians (SMG), leads to the introduction of variational parameters, for which degenerate distributions are assumed. In the proposed method all the problem unknowns are automatically estimated using variational techniques. An experimental comparison between the proposed and state of the art methods has been performed, on both synthetic and real images.

Original languageEnglish (US)
Title of host publication2013 18th International Conference on Digital Signal Processing, DSP 2013
DOIs
StatePublished - Dec 6 2013
Event2013 18th International Conference on Digital Signal Processing, DSP 2013 - Santorini, Greece
Duration: Jul 1 2013Jul 3 2013

Publication series

Name2013 18th International Conference on Digital Signal Processing, DSP 2013

Other

Other2013 18th International Conference on Digital Signal Processing, DSP 2013
CountryGreece
CitySantorini
Period7/1/137/3/13

Keywords

  • Image processing
  • Superresolution

ASJC Scopus subject areas

  • Signal Processing

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