Abstract

Recently, there has been much work developing super-resolution algorithms for combining a set of low quality images to produce a set of higher quality images. In most cases, such algorithms must first register the collection of images to a common sampling grid and then reconstruct the high resolution image. While many such algorithms have been proposed to address each one of these subproblems, no work has addressed the overall performance limits for this joint estimation problem. In this paper, we analyze the performance limits from statistical first principles using the Cramer-Rao bound. We offer insight into the fundamental bottlenecks limiting the performance of multiframe image reconstruction algorithms and hence super-resolution.

Citations