A non-stationary image prior combination in super-resolution

S. Villena*, M. Vega, R. Molina, A. K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

A new Bayesian Super-Resolution (SR) image registration and reconstruction method is proposed. The new method utilizes a prior distribution based on a general combination of spatially adaptive, or non-stationary, image filters, which includes an adaptive local strength parameter able to preserve both image edges and textures. With the application of variational techniques, the proposed method allows for the automatic estimation of all problem unknowns. An experimental comparison between state of the art methods and the proposed SR approach has been performed on both synthetic and real images.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalDigital Signal Processing: A Review Journal
Volume32
DOIs
StatePublished - Sep 2014

Keywords

  • Bayesian methods
  • Parameter estimation
  • Super-resolution
  • Total variation
  • Variational methods

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics

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