Super-resolution restoration method try to restore the spectrum of an original image above the data sampling frequency. The restoration problem is generally view as an inverse problem and lot of research focus on the inversion approach. In this paper the question of super-resolution is view from the forward model point of view. With the integration of the time dimension in a specific case of continuous shift scanning we obtain explicit expressions for the forward model and the data spectrum. We show with a simple and intuitive formalism that super-resolution can be done because the data are less aliased than a single image of the scene. Limit, condition and performance questions are also addressed.