Super-resolution image pyramid

The existing methods for the reconstruction of a super-resolution image from a sequence of undersampled and subpixel shifted images have to solve a large ill-condition equation group by approximately finding the pseudo-inverse matrix or performing many iterations to approach the solution. The former leads to a big burden of computation, and the latter causes the artifacts or noise to be stressed. In order to solve these problems, in this paper, we consider applying pyramid structure to the super-resolution of the image sequence and present a suitable pyramid framework, called Super-Resolution Image Pyramid (SRIP). Based on the imaging process of the image sequence, the proposed method divides a big back-projection into a series of different levels of small back-projections, thereby avoiding the above problems. As an example, the Iterative Back-Projection (IBP) suggested by Peleg is included in this pyramid framework. Computer simulations and error analyses are conducted and the effectiveness of the proposed framework is demonstrated. The image resolution can be improved better even in the case of severely undersampled images. In addition, the other general super-resolution methods can be easily included in this framework and done in parallel so as to meet the need of real-time processing.