Variational Bayesian Super Resolution

—In this paper, we address the super resolution (SR) problemfromasetofdegradedlowresolution(LR)imagestoobtain a high resolution (HR) image. Accurate estimation of the sub-pixel motionbetweentheLRimagessignificantlyaffectstheperformance ofthereconstructedHRimage.Inthispaper,weproposenovelsuper resolution methodswhere the HR imageandthemotionparameters 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 full...

• Type: Journal
• Year: 2011
• Where: TIP
• Authors: S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos
• Keywords: Algorithms, Bayesian Formulation, Education, Motion Parameters, TIP 2011