OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 23 — Nov. 7, 2011
  • pp: 23227–23239

Marginal blind deconvolution of adaptive optics retinal images

L. Blanco and L. M. Mugnier  »View Author Affiliations


Optics Express, Vol. 19, Issue 23, pp. 23227-23239 (2011)
http://dx.doi.org/10.1364/OE.19.023227


View Full Text Article

Enhanced HTML    Acrobat PDF (1425 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Adaptive Optics corrected flood imaging of the retina has been in use for more than a decade and is now a well-developed technique. Nevertheless, raw AO flood images are usually of poor contrast because of the three-dimensional nature of the imaging, meaning that the image contains information coming from both the in-focus plane and the out-of-focus planes of the object, which also leads to a loss in resolution. Interpretation of such images is therefore difficult without an appropriate post-processing, which typically includes image deconvolution. The deconvolution of retina images is difficult because the point spread function (PSF) is not well known, a problem known as blind deconvolution. We present an image model for dealing with the problem of imaging a 3D object with a 2D conventional imager in which the recorded 2D image is a convolution of an invariant 2D object with a linear combination of 2D PSFs. The blind deconvolution problem boils down to estimating the coefficients of the PSF linear combination. We show that the conventional method of joint estimation fails even for a small number of coefficients. We derive a marginal estimation of the unknown parameters (PSF coefficients, object Power Spectral Density and noise level) followed by a MAP estimation of the object. We show that the marginal estimation has good statistical convergence properties and we present results on simulated and experimental data.

© 2011 OSA

OCIS Codes
(010.1080) Atmospheric and oceanic optics : Active or adaptive optics
(100.3190) Image processing : Inverse problems
(100.6890) Image processing : Three-dimensional image processing
(170.4470) Medical optics and biotechnology : Ophthalmology
(100.1455) Image processing : Blind deconvolution

ToC Category:
Adaptive Optics

History
Original Manuscript: July 12, 2011
Revised Manuscript: September 16, 2011
Manuscript Accepted: September 17, 2011
Published: November 1, 2011

Virtual Issues
Vol. 7, Iss. 1 Virtual Journal for Biomedical Optics

Citation
L. Blanco and L. M. Mugnier, "Marginal blind deconvolution of adaptive optics retinal images," Opt. Express 19, 23227-23239 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-23-23227


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. J. Liang, D. R. Williams, and D. T. Miller, “Supernormal vision and high-resolution retinal imaging through adaptive optics,” J. Opt. Soc. Am. A14, 2884–2892 (1997). [CrossRef]
  2. M. Glanc, E. Gendron, F. Lacombe, D. Lafaille, J.-F. Le Gargasson, and P. Léna, “Towards wide-field retinal imaging with adaptive optics,” Opt. Commun.230, 225–238 (2004). [CrossRef]
  3. J. Rha, R. S. Jonnal, K. E. Thorn, J. Qu, Y. Zhang, and D. T. Miller, “Adaptive optics flood-illumination camera for high speed retinal imaging,” Opt. Express14, 4552–4569 (2006). [CrossRef] [PubMed]
  4. A. Roorda, F. Romero-Borja, I. William Donnelly, H. Queener, T. Hebert, and M. Campbell, “Adaptive optics scanning laser ophthalmoscopy,” Opt. Express10, 405–412 (2002). [PubMed]
  5. L. Blanc-Féraud, L. Mugnier, and A. Jalobeanu, “Blind image deconvolution,” in “Inverse Problems in Vision and 3D Tomography,”, A. Mohammad-Djafari, ed. (ISTE / John Wiley, London, 2010), chap. 3, pp. 97–121.
  6. G. R. Ayers and J. C. Dainty, “Iterative blind deconvolution and its applications,” Opt. Lett.13, 547–549 (1988). [CrossRef] [PubMed]
  7. L. M. Mugnier, T. Fusco, and J.-M. Conan, “Mistral: a myopic edge-preserving image restoration method, with application to astronomical adaptive-optics-corrected long-exposure images,” J. Opt. Soc. Am. A21, 1841–1854 (2004). [CrossRef]
  8. R. J. A. Little and D. B. Rubin, “On jointly estimating parameters and missing data by maximizing the complete-data likelihood,” The American Statistician37, 218–220 (1983). [CrossRef]
  9. J. C. Christou, A. Roorda, and D. R. Williams, “Deconvolution of adaptive optics retinal images,” J. Opt. Soc. Am. A21, 1393–1401 (2004). [CrossRef]
  10. G. Harikumar and Y. Bresler, “Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms,” IEEE Trans. Image Processing8, 202–219 (1999). [CrossRef]
  11. J. Idier, L. Mugnier, and A. Blanc, “Statistical behavior of joint least square estimation in the phase diversity context,” IEEE Trans. Image Processing14, 2107–2116 (2005). [CrossRef]
  12. E. Lehmann, Theory of point estimation (John Wiley, New York, NY, 1983).
  13. A. Blanc, L. M. Mugnier, and J. Idier, “Marginal estimation of aberrations and image restoration by use of phase diversity,” J. Opt. Soc. Am. A20, 1035–1045 (2003). [CrossRef]
  14. Y. Goussard, G. Demoment, and J. Idier, “A new algorithm for iterative deconvolution of sparse spike,” in “ICASSP,” (1990), pp. 1547–1550.
  15. 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). [CrossRef]
  16. É. Thiébaut, “Optimization issues in blind deconvolution algorithms,” in “Astronomical Data Analysis II,”, vol. 4847, J.-L. Starck and F. D. Murtagh, eds. (Proc. Soc. Photo-Opt. Instrum. Eng., 2002), vol. 4847, pp. 174–183.

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