OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 18, Iss. 4 — Apr. 1, 2001
  • pp: 862–872

Myopic deconvolution from wave-front sensing

Laurent M. Mugnier, Clélia Robert, Jean-Marc Conan, Vincent Michau, and Sélim Salem  »View Author Affiliations

JOSA A, Vol. 18, Issue 4, pp. 862-872 (2001)

View Full Text Article

Acrobat PDF (609 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Deconvolution from wave-front sensing is a powerful and low-cost high-resolution imaging technique designed to compensate for the image degradation due to atmospheric turbulence. It is based on a simultaneous recording of short-exposure images and wave-front sensor (WFS) data. Conventional data processing consists of a sequential estimation of the wave fronts given the WFS data and then of the object given the reconstructed wave fronts and the images. However, the object estimation does not take into account the wave-front reconstruction errors. A joint estimation of the object and the respective wave fronts has therefore been proposed to overcome this limitation. The aim of our study is to derive and validate a robust joint estimation approach, called myopic deconvolution from wave-front sensing. Our estimator uses all data simultaneously in a coherent Bayesian framework. It takes into account the noise in the images and in the WFS measurements and the available <i>a priori</i> information on the object to be restored as well as on the wave fronts. Regarding the <i>a priori</i> information on the object, an edge-preserving prior is implemented and validated. This method is validated on simulations and on experimental astronomical data.

© 2001 Optical Society of America

OCIS Codes
(010.1330) Atmospheric and oceanic optics : Atmospheric turbulence
(010.7350) Atmospheric and oceanic optics : Wave-front sensing
(100.1830) Image processing : Deconvolution
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(110.6770) Imaging systems : Telescopes

Laurent M. Mugnier, Clélia Robert, Jean-Marc Conan, Vincent Michau, and Sélim Salem, "Myopic deconvolution from wave-front sensing," J. Opt. Soc. Am. A 18, 862-872 (2001)

Sort:  Author  |  Year  |  Journal  |  Reset


  1. J.-C. Fontanella, “Analyse de surface d’onde, déconvolution et optique active,” J. Mod. Opt. 16, 257–268 (1985).
  2. J. Primot, G. Rousset, and J.-C. Fontanella, “Image deconvolution from wavefront sensing: atmospheric turbulence simulation cell results,” in Very Large Telescopes and Their Instrumentation, Vol. II, M.-H. Ulrich, ed., ESO Conf. and Workshop Proc. No. 30 (European Southern Observatory, Garching, Germany, 1988), pp. 683–692.
  3. J. Primot, G. Rousset, and J.-C. Fontanella, “Deconvolution from wavefront sensing: a new technique for compensating turbulence-degraded images,” J. Opt. Soc. Am. A 7, 1598–1608 (1990).
  4. J. D. Gonglewski, D. G. Voelz, J. S. Fender, D. C. Dayton, B. K. Spielbusch, and R. E. Pierson, “First astronomical application of postdetection turbulence compensation: images of α Aurigae, ν Ursae Majoris, and α Geminorum using self-referenced speckle holography,” Appl. Opt. 29, 4527–4529 (1990).
  5. T. Marais, V. Michau, G. Fertin, J. Primot, and J. C. Fontanella, “Deconvolution from wavefront sensing on a 4 m telescope,” in High-Resolution Imaging by Interferometry II, J. M. Beckers and F. Merkle, eds., ESO Conf. and Workshop Proc. No. 39 (European Southern Observatory, Garching, Germany, 1992), pp. 589–597.
  6. F. Roddier, “Passive versus active methods in optical interferometry,” in High-Resolution Imaging by Interferometry Part II, F. Merkle, ed., ESO Conf. and Workshop Proc. No. 29 (European Southern Observatory, Garching, Germany, 1988), pp. 565–574.
  7. M. C. Roggemann, C. A. Hyde, and B. M. Welsh, “Fourier phase spectrum estimation using deconvolution from wavefront sensing and bispectrum reconstruction,” in Adaptive Optics, Vol. 12 of 1996 OSA Technical Digest Series (Optical Society of America, Washington, D.C., 1996), pp. 133–135.
  8. D. Dayton, J. Gonglewski, and S. Rogers, “Experimental measurements of estimator bias and the signal-to-noise ratio for deconvolution from wave-front sensing,” Appl. Opt. 36, 3895–3902 (1997).
  9. M. C. Roggemann and B. M. Welsh, “Signal to noise ratio for astronomical imaging by deconvolution from wave-front sensing,” Appl. Opt. 33, 5400–5414 (1994).
  10. M. C. Roggemann, B. M. Welsh, and J. Devey, “Biased estimators and object-spectrum estimation in the method of deconvolution from wave-front sensing,” Appl. Opt. 33, 5754–5763 (1994).
  11. J.-M. Conan, V. Michau, and G. Rousset, “Signal-to-noise ratio and bias of various deconvolution from wavefront sensing estimators,” in Image Propagation through the Atmosphere, J. C. Dainty and L. R. Bissonnette, eds., Proc. SPIE 2828, 332–339 (1996).
  12. T. J. Schulz, “Estimation-theoretic approach to the deconvolution of atmospherically degraded images with wavefront sensor measurements,” in Digital Image Recovery and Synthesis II, P. S. Idell, ed., Proc. SPIE 2029, 311–320 (1993).
  13. L. M. Mugnier, J.-M. Conan, V. Michau, and G. Rousset, “Imagerie travers la turbulence par déconvolution myope multi-trame,” in Seizième Colloque sur le Traitement du Signal et des Images, J.-M. Chassery and C. Jutten, eds. (Gretsi, Grenoble, France, 1997), pp. 567–570.
  14. D. C. Dayton, S. C. Sandven, and J. D. Gonglewski, “Expectation maximization approach to deconvolution from wavefront sensing,” in Image Reconstruction and Restoration II, T. J. Schulz, ed., Proc. SPIE 3170, 16–24 (1997).
  15. L. M. Mugnier, C. Robert, J.-M. Conan, V. Michau, and S. Salem, “Regularized multiframe myopic deconvolution from wavefront sensing,” in Propagation through the Atmosphere III, M. C. Roggemann and L. R. Bissonnette, eds., Proc. SPIE 3763, 134–144 (1999).
  16. T. Fusco, J.-M. Conan, V. Michau, L. M. Mugnier, and G. Rousset, “Phase estimation for large field of view: application to multiconjugate adaptive optics,” in Propagation-through the Atmosphere III, M. C. Roggemann and L. R. Bissonnette, eds., Proc. SPIE 3763, 125–133 (1999).
  17. T. Fusco, J.-M. Conan, V. Michau, L. Mugnier, and G. Rousset, “Efficient phase estimation for large-field-of-view adaptive optics,” Opt. Lett. 24, 1472–1474 (1999).
  18. R. J. Noll, “Zernike polynomials and atmospheric turbulence,” J. Opt. Soc. Am. 66, 207–211 (1976).
  19. C. M. Titterington, “General structure of regularization procedures in image reconstruction,” Astron. Astrophys. 144, 381–387 (1985).
  20. G. Demoment, “Image reconstruction and restoration: overview of common estimation structures and problems,” IEEE Trans. Acoust., Speech, Signal Process. 37, 2024–2036 (1989).
  21. E. P. Wallner, “Optimal wave-front correction using slope measurements,” J. Opt. Soc. Am. 73, 1771–1776 (1983).
  22. B. M. Welsh and R. N. VonNiederhausern, “Performance analysis of the self-referenced speckle-holography image-reconstruction technique,” Appl. Opt. 32, 5071–5078 (1993).
  23. H. L. Van Trees, Detection, Estimation, and Modulation Theory (Wiley, New York, 1968).
  24. P. A. Bakut, V. E. Kirakosyants, V. A. Loginov, C. J. Solomon, and J. C. Dainty, “Optimal wavefront reconstruction form a Shack–Hartmann sensor by use of a Bayesian algorithm,” Opt. Commun. 109, 10–15 (1994).
  25. S. D. Ford, B. M. Welsh, and M. C. Roggemann, “Constrained least-squares estimation in deconvolution from wave-front sensing,” Opt. Commun. 151, 93–100 (1998).
  26. J.-M. Conan, L. M. Mugnier, T. Fusco, V. Michau, and G. Rousset, “Myopic deconvolution of adaptive optics images by use of object and point-spread function power spectra,” Appl. Opt. 37, 4614–4622 (1998).
  27. L. P. Yaroslavsky and H. J. Caulfield, “Deconvolution of multiple images of the same object,” Appl. Opt. 33, 2157–2162 (1994).
  28. P. J. Green, “Bayesian reconstructions from emission tomography data using a modified EM Algorithm,” IEEE Trans. Med. Imaging 9, 84–93 (1990).
  29. C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Image Process. 2, 296–310 (1993).
  30. W. J. Rey, Introduction to Robust and Quasi-Robust Statistical Methods (Springer-Verlag, Berlin, 1983).
  31. S. Brette and J. Idier, “Optimized single site update algorithms for image deblurring,” in Proceedings of the Interna-tional Conference on Image Processing (IEEE Computer Society Press, Los Alamitos, Calif., 1996), pp. 65–68.
  32. J.-M. Conan, T. Fusco, L. M. Mugnier, E. Kersalé, and V. Michau, “Deconvolution of adaptive optics images with imprecise knowledge of the point spread function: results on astronomical objects,” in Astronomy with Adaptive Optics: Present Results and Future Programs, D. Bonaccini, ed., ESO Conf. and Workshop Proc. No. 56 (European Southern Observatory, Garching, Germany, 1999), pp. 121–132.
  33. T. Fusco, J.-P. Véran, J.-M. Conan, and L. Mugnier, “Myopic deconvolution method for adaptive optics images of stellar fields,” Astron. Astrophys., Suppl. Ser. 134, 1–10 (1999).
  34. T. J. Schulz, “Multiframe blind deconvolution of astronomical images,” J. Opt. Soc. Am. A 10, 1064–1073 (1993).
  35. E. Thiébaut and J.-M. Conan, “Strict a priori constraints for maximum-likelihood blind deconvolution,” J. Opt. Soc. Am. A 12, 485–492 (1995).
  36. Groupe Problèmes Inverses, “GPAV: une grande oeuvre collective,” Internal Report, Laboratoire des Signaux et Systèmes (Centre National de la Recherche Scientifique/Supélec/Université Paris-Sud, Paris, 1997).
  37. D. P. Bertsekas, Nonlinear Programming (Athena Scientific, Belmont, Mass., 1995).
  38. D. G. Luenberger, Introduction to Linear and Nonlinear Programming (Addison-Wesley, Reading, Mass., 1973).
  39. Y.-L. You and M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Processing 5, 416–428 (1996).
  40. N. Roddier, “Atmospheric wavefront simulation using Zernike polynomials,” Opt. Eng. 29, 1174–1180 (1990).
  41. D. W. Tyler and C. L. Matson, “Speckle imaging detector optimization and comparison,” Opt. Eng. 32, 864–869 (1993).
  42. E. Thiébaut, “Speckle imaging with the bispectrum and without reference star,” in International Astronomical Union Symposium on Very High Angular Resolution Imaging, R. J. G. Robertson and W. J. Tango, eds. (Kluwer Academic, Dordrecht, The Netherlands, 1994), Vol. 158, p. 209.
  43. R. G. Paxman, T. J. Schulz, and J. R. Fienup, “Joint estimation of object and aberrations by using phase diversity,” J. Opt. Soc. Am. A 9, 1072–1085 (1992).

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