Joint Image Registration and Super-Resolution From Low-Resolution Images With Zooming Motion

This paper proposes a new framework for joint image registration and high-resolution (HR) image reconstruction from multiple low-resolution (LR) observations with zooming motion. Conventional super-resolution (SR) methods typically formulate the SR problem as a two-stage process, namely, image registration followed by HR reconstruction. An important step in image SR is the effective estimation of motion parameters. However, the registration algorithms in these two-stage processes experience various degrees of errors. This could degrade the quality of subsequent HR reconstruction. In view of this, this paper presents a new approach that performs joint image registration and SR reconstruction. The proposed iterative SR framework enables the HR image and motion parameters to be estimated simultaneously and progressively. This could increase the potential SR improvement as more accurate estimates of motion parameters could be obtained iteratively. Experimental results show that the proposed method is effective in performing image registration and SR for simulated and real-life images and videos.