Pyramid-based super-resolution of the undersampled and subpixel shifted image sequence

The existing methods for the reconstruction of a super-resolution image from the undersampled and subpixel shifted image sequence have to solve a large ill-condition equation group by approximately finding the 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 over-stressed. So they are rarely implemented in practical use. In order to solve these problems, in this article, 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), and determine the pyramid back-projection. Pyramid structure and methods are widely used in image processing and computer vision. But we have not found their applications to the super-resolution in literatures. We give a complete description for SRIP. As an example, the Iterative Back-Projection (IBP) suggested by Peleg (1991, 1993) is integrated in this pyramid framework. The experiments and the error analysis are performed to show the effectiveness of this framework. 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 integrated in this framework so that they can be done in parallel so as to meet the need of real-time processing. (C) 2003 Wiley Periodicals, Inc.