Unsupervised 3D deconvolution method for retinal imaging: principle and preliminary validation on experimental data
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.
- Publication:
-
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVI
- Pub Date:
- February 2009
- DOI:
- 10.1117/12.810267
- Bibcode:
- 2009SPIE.7184E..0VC