Multi-frame super-resolution from observations with zooming motion

This paper proposes a new multi-frame super-resolution (SR) approach to reconstruct a high-resolution (HR) image from low-resolution (LR) images with zooming motion. Existing SR methods often assume that the relative motion between acquired LR images consists of only translation and possibly rotation. This restricts the application of these methods in cases when there is zooming motion among the LR images. There are currently only a few methods focusing on zooming SR. Their formulation usually ignores the registration errors or assumes them to be negligible during the HR reconstruction. In view of this, this paper presents a new SR reconstruction approach to handle a more flexible motion model including translation, rotation and zooming. An iterative framework is developed to estimate the motion parameters and HR image progressively. Both simulated and real-life experiments show that the proposed method is effective in performing image SR.