Bayesian combination of sparse and non-sparse priors in image super resolution

نویسندگان

  • Salvador Villena
  • Miguel Vega
  • S. Derin Babacan
  • Rafael Molina
  • Aggelos K. Katsaggelos
چکیده

In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. Two sparse image priors, a Total Variation (TV) prior and a prior based on the `1 norm of horizontal and vertical first order differences (f.o.d.), are combined with a non-sparse Simultaneous Auto Regressive (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 approximation 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 minimizes a linear convex combination of Kullback-Leibler (KL) divergences. We find this distribution in closed form. The estimated HR images are compared with the ones obtained by other SR reconstruction methods.

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عنوان ژورنال:
  • Digital Signal Processing

دوره 23  شماره 

صفحات  -

تاریخ انتشار 2013