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Proceedings Paper

Unsupervised 3D deconvolution method for retinal imaging: principle and preliminary validation on experimental data
Author(s): G. Chenegros; L. M. Mugnier; C. Alhenc-Gelas; F. Lacombe; M. Glanc; M. Nicolas
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Paper Abstract

High resolution wide-field imaging of the human retina calls for a 3D deconvolution. In this communication, we report on a regularized 3D deconvolution method, developed in a Bayesian framework in view of retinal imaging, which is fully unsupervised, i.e., in which all the usual tuning parameters, a.k.a. "hyper-parameters", are estimated from the data. The hyper-parameters are the noise level and all the parameters of a suitably chosen model for the object's power spectral density (PSD). They are estimated by a maximum likelihood (ML) method prior to the deconvolution itself. This 3D deconvolution method takes into account the 3D nature of the imaging process, can take into account the non-homogeneous noise variance due to the mixture of photon and detector noises, and can enforce a positivity constraint on the recovered object. The performance of the ML hyper-parameter estimation and of the deconvolution are illustrated both on simulated 3D retinal images and on non-biological 3D experimental data.

Paper Details

Date Published: 3 March 2009
PDF: 12 pages
Proc. SPIE 7184, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVI, 71840V (3 March 2009); doi: 10.1117/12.810267
Show Author Affiliations
G. Chenegros, ONERA-PHASE (France)
L. M. Mugnier, ONERA-PHASE (France)
C. Alhenc-Gelas, ONERA-PHASE (France)
F. Lacombe, Mauna Kea Technologies (France)
M. Glanc, Observatoire de Paris à Meudon, LESIA-PHASE (France)
M. Nicolas, ONERA-PHASE (France)
Observatoire de Paris à Meudon, LESIA-PHASE (France)

Published in SPIE Proceedings Vol. 7184:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVI
Jose-Angel Conchello; Carol J. Cogswell; Tony Wilson, Editor(s)

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