A MAP Approach for Joint Image Registration, Blur Identification and Super Resolution

Image super-resolution reconstruction (SRR) refers to a signal processing approach which produces a high-resolution (HR) image from observed multiple low-resolution (LR) images. In this paper, we propose a joint MAP formulation combining image registration, blur identification, and SRR together to deal with heavy aliasing in the observed LR images. A cyclic coordinate decent optimization procedure is used to solve the formulation, in which the registration parameters, blurring information, and HR image are found in an alternate manner given the others, respectively. The proposed algorithm has been tested on a synthetic image sequence. The experiment results and error analyses verify the efficacy of this algorithm.