Reconstruction-based super-resolution algorithms use either sub-pixel shifts or relative blur among low-resolution observations as a cue to obtain a high-resolution image. In this paper, we propose a super-resolution algorithm that exploits the information available in the low-resolution observations due to both sub-pixel shifts and relative blur to yield a better quality image. Performance analysis is carried out based on the Cramer-Rao lower bound. Several experimental results on synthetic and real images are given for validation.