Variational bayesian super resolution

S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos

Research output: Contribution to journalArticle

189 Scopus citations


In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.

Original languageEnglish (US)
Article number5585756
Pages (from-to)984-999
Number of pages16
JournalIEEE Transactions on Image Processing
Issue number4
StatePublished - Apr 1 2011


  • Bayesian methods
  • parameter estimation
  • super resolution
  • total variation
  • variational methods

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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