OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 3 — Feb. 29, 2012

Using artificial neural networks for open-loop tomography

James Osborn, Francisco Javier De Cos Juez, Dani Guzman, Timothy Butterley, Richard Myers, Andrés Guesalaga, and Jesus Laine  »View Author Affiliations


Optics Express, Vol. 20, Issue 3, pp. 2420-2434 (2012)
http://dx.doi.org/10.1364/OE.20.002420


View Full Text Article

Enhanced HTML    Acrobat PDF (982 KB) Open Access





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques.

© 2012 OSA

OCIS Codes
(010.1080) Atmospheric and oceanic optics : Active or adaptive optics
(010.1330) Atmospheric and oceanic optics : Atmospheric turbulence

ToC Category:
Adaptive Optics

History
Original Manuscript: October 6, 2011
Revised Manuscript: December 22, 2011
Manuscript Accepted: December 22, 2011
Published: January 19, 2012

Virtual Issues
Vol. 7, Iss. 3 Virtual Journal for Biomedical Optics

Citation
James Osborn, Francisco Javier De Cos Juez, Dani Guzman, Timothy Butterley, Richard Myers, Andrés Guesalaga, and Jesus Laine, "Using artificial neural networks for open-loop tomography," Opt. Express 20, 2420-2434 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-20-3-2420


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. M. Le Louarn and N. Hubin, “Wide-field adaptive optics for deep-field spectroscopy in the visible,” Mon. Not. R. Astron. Soc.349(3), 1009–1018 (2004). [CrossRef]
  2. J. M. Beckers, “Detailed compensation of atmospheric seeing using multiconjugate adaptive optics,” Proc. SPIE1114, 215–217 (1989).
  3. F. Hammer, F. Sayède, E. Gendron, T. Fusco, D. Burgarella, V. Cayatte, J.-M. Conan, F. Courbin, H. Flores, I. Guinouard, L. Jocou, A. Lançon, G. Monnet, M. Mouhcine, F. Rigaud, D. Rouan, G. Rousset, V. Buat, and F. Zamkotsian, “The FALCON Concept: Multi-Object Spectroscopy Combined with MCAO in Near-IR,” in Scientific Drivers for ESO Future VLT/VLTI Instrumentation, J. Bergeron and G. Monnet, eds.(Springer-Verlag, 2002), p. 139. [CrossRef]
  4. F. Assémat, E. Gendron, and F. Hammer, “The FALCON concept: multi-object adaptive optics and atmospheric tomography for integral field spectroscopy – principles and performance on an 8-m telescope,” Mon. Not. R. Astron. Soc.376, 287–312 (2007). [CrossRef]
  5. T. Morris, Z. Hubert, R. Myers, E. Gendron, A. Longmore, G. Rousset, G. Talbot, T. Fusco, N. Dipper, F. Vidal, D. Henry, D. Gratadour, T. Butterley, F. Chemla, D. Guzman, P. Laporte, E. Younger, A. Kellerer, M. Harrison, M. Marteaud, D. Geng, A. Basden, A. Guesalaga, C. Dunlop, S. Todd, C. Robert, K. Dee, C. Dickson, N. Vedrenne, A. Greenaway, B. Stobie, H. Dalgarno, and J. Skvarc, “CANARY: The NGS/LGS MOAO demonstrator for EAGLE,” 1st AO4ELT conference p. 08003 (2010).
  6. E. Gendron, F. Vidal, M. Brangier, T. Morris, Z. Hubert, A. Basden, G. Rousset, R. M. Myers, F. Chemla, A. Longmore, T. Butterley, N. Dipper, C. Dunlop, D. Geng, D. Gratador, D. Henry, P. Laporte, N. Looker, D. Perret, A. Sevin, G. Talbot, and E. Younger, “MOAO first on-sky demonstration with CANARY,” Astron. Astrophys.L2(529) (2011).
  7. R. Avila, E. Carrasco, F. Ibañez, J. Vernin, J. L. Prieur, and D. X. Cruz, “Generalized SCIDAR Measurements at San Pedro Mártir. II. Wind Profile Statistics,” Publ. Astron. Soc. Pac.118, 503–515 (2006). [CrossRef]
  8. J. R. P. Angel, P. Wizinowich, M. Lloyd-Hart, and D. Sandler, “Adaptive optics for array telescopes using neural-network techniques,” Nature348, 221–224 (1990). [CrossRef]
  9. D. G. Sandler, T. K. Barrett, D. A. Palmer, R. Q. Fugate, and W. J. Wild, “Use of a neural network to control an adaptive optics system for an astronomical telescope,” Nature351, 300–302 (1991). [CrossRef]
  10. M. Lloyd-Hart, P. Wizinowich, B. McLeod, D. Wittman, D. Colucci, R. Dekany, D. McCarthy, J. R. P. Angel, and D. Sandler, “First results of an on-line adaptive optics system with atmospheric wavefront sensing by an artifical neural network,” Astrophys. J.390(1), L41–L44 (1992). [CrossRef]
  11. D. A. Montera, M. C. Welsh, B. M. Roggemann, and D. W. Ruck, “Processing wave-front-sensors slope measurements using artificial neural networks,” Appl. Opt.35(21), 4238–4251 (1996). [CrossRef] [PubMed]
  12. M. Lloyd-Hart and P. McGuire, “Spatio-temporal prediction for adaptive optics wavefront reconstructors,” in Proc. European Southern Observatory Conf. on Adaptive Optics, pp. 95–102 (1995).
  13. S. J. Weddell and R. Y. Webb, “Dynamic Artificial Neural Networks for Centroid Prediction in Astronomy,” inProc. of the Sixth International Conference on Hybrid Intelligent Systems, pp. 68 (2006). [CrossRef]
  14. S. J. Weddell and R. Y. Webb, “A Neural Network Architecture for the Reconstruction of Turbulence Degraded Point Spread Functions,” in Proc. Image and Vision Computing New Zealand, pp. 103–108 (2007).
  15. D. Guzmán, F. J. Juez, R. Myers, A. Guesalaga, and F. Lasheras, “Modeling a MEMS deformable mirror using non-parametric estimation techniques,” Opt. Express18(20), 21356–21369 (2010). [CrossRef] [PubMed]
  16. B. L. Ellerbroek, “First-order performance evaluation of adaptive-optics systems for atmospheric-turbulence compensation in extended-field-of-view astronomical telescopes,” J. Opt. Soc. Am. A11(2), 783–805 (1994). [CrossRef]
  17. T. Fusco, J. Conan, G. Rousset, L. M. Mugnier, and V. Michau, “Optimal wave-front reconstruction strategies for multiconjugate adaptive optics,” J. Opt. Soc. Am. A18(10), 2527–2538 (2001). [CrossRef]
  18. J. W. Wild, E. J. Kibblewhite, and R. Vuilleumier, “Sparse matrix wave-front estimators for adaptive-optics systems for large ground-based telescopes,” Opt. Lett.20(9), 955–957 (1995). [CrossRef] [PubMed]
  19. E. Thiébaut and M. Tallon, “Fast minimum variance wavefront reconstruction for extremely large telescopes,” J. Opt. Soc. Am. A27(5), 1046–1059 (2010). [CrossRef]
  20. F. Vidal, E. Gendron, and G. Rousset, “Tomography approach for multi-object adaptive optics,” J. Opt. Soc. Am. A.27(11), 253–264 (2010). [CrossRef]
  21. R. W. Wilson, “SLODAR: measuring optical turbulence altitude with a Shack–Hartmann wavefront sensor,” Mon. Not. R. Astron. Soc.337(1), 103–108 (2002). [CrossRef]
  22. K. Huarng and T. H.-K. Yu, “The application of neural networks to forecast fuzzy time series,” Physica A, 363(2), 481–491 (2006). [CrossRef]
  23. K. Swingler, Applying Neural Networks: A Practicle Guide (Academic Press, 1996).
  24. J. W. Denton, “How good are neural networks for causal forecasting?” J. Bus. Forecast. Methods Syst.14(2), 17–20 (1995).
  25. S. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall, 1999).
  26. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature323, 533–536 (1986). [CrossRef]
  27. L. Bottaci, P. J. Drew, J. E. Hartley, M. B. Hadfield, R. Farouk, P. W. Lee, I. M. Macintyre, G. S. Duthie, and J. R. Monson, “Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions,” The Lancet350(9076), 469–472 (1997). [CrossRef]
  28. S. Tamura, “An analysis of a noise reduction neural network,” in Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on, pp. 2001–2004 vol.3 (1989).
  29. R. W. Wilson and C. R. Jenkins, “Adaptive Optics for astronomy: theoretical performance and limitations,” Mon. Not. R. Astron. Soc.268, 39–61 (1996).
  30. M. Hänggi and G. S. Moschytz, Cellular Neural Networks: Analysis, Desgn and Optimization (Kluwer Academic Publishers, 2000).
  31. J. Misra and I. Saha, “Artificial neural networks in hardware: A survey of two decades of progress,” Neurocomputing74(1–3), 239–255 (2010). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited