Phase unwrapping has been and still is a cumbersome concern that involves the resolution of several different problems. When dealing with two-dimensional phase unwrapping in fringe analysis, the final objective is, in many cases, the realization of that analysis in real time. Many algorithms have been developed to carry out the unwrapping process, with some giving satisfactory results even when high levels of noise are present in the image. However, these algorithms are often time consuming and far removed from the goal of real-time fringe analysis. A new approach to the construction of a simple and fast algorithm for two-dimensional unwrapping that has considerable potential for parallel implementation is presented.
Parallel computers differ from conventional serial computers in that they can, in a variety of ways, perform more than one operation at a time. Parallel processing, the application of parallel computers, has been successfully utilized in many fields of science and technology. The purpose of this paper is to review efforts to use parallel processing for statistical computing. We present some technical background, followed by a review of the literature that relates parallel computing to statistics. The review material focuses explicitly on statistical methods and applications, rather than on conventional mathematical techniques. Thus, most of the review material is drawn from statistics publications. We conclude by discussing the nature of the review material and considering some possibilities for the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.