3 March 2009 Unsupervised 3D deconvolution method for retinal imaging: principle and preliminary validation on experimental data
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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.
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G. Chenegros, G. Chenegros, L. M. Mugnier, L. M. Mugnier, C. Alhenc-Gelas, C. Alhenc-Gelas, F. Lacombe, F. Lacombe, M. Glanc, M. Glanc, M. Nicolas, M. Nicolas, } "Unsupervised 3D deconvolution method for retinal imaging: principle and preliminary validation on experimental data", Proc. SPIE 7184, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVI, 71840V (3 March 2009); doi: 10.1117/12.810267; https://doi.org/10.1117/12.810267
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