L1-norm multi-frame super-resolution from images with zooming motion

This paper proposes a new image super-resolution (SR) approach to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images with zooming motion. Most conventional SR image reconstruction methods assume that the motion among different images consists of only translation and possibly rotation. This in-plane motion model, however, is not practical in some applications, when relative zooming exists among the acquired LR images. In view of this, this paper presents a new SR method that addresses a motion model including both in-plane motion (e.g. translation and rotation) and zooming motion. Based on this model, a maximum a posteriori (MAP) based SR algorithm using L1-norm optimization is proposed. Experimental results show that the proposed algorithm based on the new motion model performs well in terms of visual evaluation and quantitative measurement.