A Bayesian Estimation Approach to Super-Resolution Reconstruction for Face Images

Most previous super-resolution (SR) approaches are implemented with two individual cascade steps, image registration and image fusion, which handicaps the incorporation of the structural information of the objects of interest, e.g. human faces, into SR in a parallel way. This prior information is beneficial to either robust motion estimation or fusion with higher quality. In this paper, SR reconstruction is formulated as Bayesian state estimation of location and appearance parameters of a face model. In addition, a sequential Monte Carlo (SMC) based algorithm is proposed to achieve the probabilistic state estimation, i.e. SR reconstruction in our formulation. Image alignment and image fusion are combined into one unified framework in the proposed approach, in which the prior information from the face model is incorporated into both registration and fusion process of SR. Experiments performed on synthesized frontal face sequences show that the proposed approach gains superior performance in registration as well as reconstruction.