Translator Disclaimer
3 March 2009 Unsupervised 3D deconvolution method for retinal imaging: principle and preliminary validation on experimental data
Author Affiliations +
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
G. Chenegros, L. M. Mugnier, C. Alhenc-Gelas, F. Lacombe, M. Glanc, and 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); https://doi.org/10.1117/12.810267
PROCEEDINGS
12 PAGES


SHARE
Advertisement
Advertisement
Back to Top