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Dirk Robinson

Publications and source records attributed to Dirk Robinson.

2 recordsLinked to original sources
PubMed2006

Statistical performance analysis of super-resolution.

Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of low-quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Cramér-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration.

Algorithms↗
PubMed2004

Fundamental performance limits in image registration.

The task of image registration is fundamental in image processing. It often is a critical preprocessing step to many modern image processing and computer vision tasks, and many algorithms and techniques have been proposed to address the registration problem. Often, the performances of these techniques have been presented using a variety of relative measures comparing different estimators, leaving open the critical question of overall optimality. In this paper, we present the fundamental performance limits for the problem of image registration as derived from the Cramer-Rao inequality. We compare the experimental performance of several popular methods with respect to this performance bound, and explain the fundamental tradeoff between variance and bias inherent to the problem of image registration. In particular, we derive and explore the bias of the popular gradient-based estimator showing how widely used multiscale methods for improving performance can be explained with this bias expression. Finally, we present experimental simulations showing the general rule-of-thumb performance limits for gradient-based image registration techniques.

Algorithms↗