Super-resolution with continuous scan shift

Super-resolution methods aimed to restore the spectrum of an original image above the half sampling frequency. The restoration problem is generally viewed as an inverse problem and a lot of research focus on the inversion approach. In this paper the question of super-resolution is addressed with only the forward model point of view. With the addition of the time dimension in an specific case of continuous shift scanning we obtain explicit expression for the forward model and the data spectrum. We show with a new, simple and rigorous formalism that super-resolution can be done because the whole set of data is less aliased than a single image of the scene. Moreover, limits, conditions and performance questions are also addressed as well as perspectives on sampling conditions.