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.