New results on performance analysis of super-resolution image reconstruction

In this paper, we present new results in performance analysis of super-resolution (SR) image reconstruction. We investigate bounds on the improvement in resolution that can be achieved and its relation to the image sequence. We derive lower bounds on the resolution enhancement factor based on a frequency-domain SR algorithm. We subsequently show that the bounds remain valid for other SR algorithms. Moreover, we consider an image sequence model in the presence of affine motion. We demonstrate theoretically and experimentally that incorporation of affine motion into the image model can be used to increase the enhancement factor in comparison to purely translational motion. Finally, we discuss the extension of the performance bounds to temporal super-resolution methods and its implications on the image sequence.