OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 35, Iss. 21 — Jul. 20, 1996
  • pp: 4238–4251

Processing wave-front-sensor slope measurements using artificial neural networks

Dennis A. Montera, Byron M. Welsh, Michael C. Roggemann, and Dennis W. Ruck  »View Author Affiliations


Applied Optics, Vol. 35, Issue 21, pp. 4238-4251 (1996)
http://dx.doi.org/10.1364/AO.35.004238


View Full Text Article

Enhanced HTML    Acrobat PDF (344 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

For adaptive-optics systems to compensate for atmospheric turbulence effects, the wave-front perturbation must be measured with a wave front sensor (WFS), and key parameters of the atmosphere and the adaptive-optics system must be known. Two parameters of particular interest include the Fried coherence length r0 and the WFS slope measurement error. Statistics-based optimal techniques, such as the minimum variance phase reconstructor, have been developed to improve the imaging performance of adaptive-optics systems. However, these statistics-based models rely on knowledge of the current state of the key parameters. Neural networks provide nonlinear solutions to adaptive-optics problems while offering the possibility of adapting to changing seeing conditions. We address the use of neural networks for three tasks: (1) to reduce the WFS slope measurement error, (2) to estimate the Fried coherence length r0, and (3) to estimate the variance of the WFS slope measurement error. All of these tasks are accomplished by using only the noisy WFS measurements as input. Where appropriate, we compare our method with classical statistics-based methods to determine if neural networks offer true benefits in performance. Although a statistics-based method is found to perform better than a neural network in reducing WFS slope measurement error, neural networks perform better in estimating the variance of the WFS slope measurement error, and both methods perform well in estimating r0.

© 1996 Optical Society of America

History
Original Manuscript: October 2, 1995
Revised Manuscript: February 12, 1996
Published: July 20, 1996

Citation
Dennis A. Montera, Byron M. Welsh, Michael C. Roggemann, and Dennis W. Ruck, "Processing wave-front-sensor slope measurements using artificial neural networks," Appl. Opt. 35, 4238-4251 (1996)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-35-21-4238

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

If you are accessing the full text through a member bundle, please use the Enhanced HTML link to gain access to the citation lists and other restricted features. Note that accessing both the PDF and HTML versions of an article will count as only one download against your account.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

If you are accessing the full text through a member bundle, please use the Enhanced HTML link to gain access to the citation lists and other restricted features. Note that accessing both the PDF and HTML versions of an article will count as only one download against your account.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

If you wish to use one of your free member downloads to view the figures, click "Enhanced HTML" above and access the figures from the article itself or from the navigation tab.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

If you are accessing the full text through a member bundle, please use the Enhanced HTML link to gain access to the citation lists and other restricted features. Note that accessing both the PDF and HTML versions of an article will count as only one download against your account.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited