Image prior combination in super-resolution image registration & reconstruction

Salvador Villena*, Miguel Vega, S. Derin Babacan, Rafael Molina, Aggelos K Katsaggelos

*Corresponding author for this work

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

7 Scopus citations

Abstract

In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. A sparse image prior based on the horizontal and vertical first order differences is combined with a nonsparse SAR prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational posterior distribution approximation on each posterior will produce as many posterior approximations as priors we want to combine. A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimize a linear convex combination of the Kullback-Leibler (KL) divergences associated with each posterior distribution. We find this distribution in closed form. The estimated HR images are compared with images provided by other SR reconstruction methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
Pages355-360
Number of pages6
DOIs
StatePublished - Nov 24 2010
Event2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland
Duration: Aug 29 2010Sep 1 2010

Publication series

NameProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

Other

Other2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
CountryFinland
CityKittila
Period8/29/109/1/10

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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