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Proceedings Paper

3D deconvolution of adaptive-optics corrected retinal images
Author(s): G. Chenegros; L. M. Mugnier; F. Lacombe; M. Glanc
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Paper Abstract

We report on a deconvolution method developed in a Bayesian framework for adaptive-optics corrected images of the human retina. The method takes into account the three-dimensional nature of the imaging process; it incorporates a positivity constraint and a regularization metric in order to avoid uncontrolled noise amplification. This regularization metric is designed to simultaneously smooth noise out and preserve edges, while staying convex in order to keep the solution unique. We demonstrate the effectiveness of the method, and in particular of the edge-preserving regularization, on realistic simulated data.

Paper Details

Date Published: 23 February 2006
PDF: 8 pages
Proc. SPIE 6090, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII, 60900P (23 February 2006); doi: 10.1117/12.645233
Show Author Affiliations
G. Chenegros, ONERA (France)
L. M. Mugnier, ONERA (France)
F. Lacombe, Mauna Kea Technologies (France)
M. Glanc, Lab. d'Études Spatiales et d'Instrumentation en Astrophysique, Observatoire de Paris-Meudon (France)

Published in SPIE Proceedings Vol. 6090:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII
Jose-Angel Conchello; Carol J. Cogswell; Tony Wilson, Editor(s)

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