  Optical endomicroscopy (OEM) is an emerging technology platform with
preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles
has the potential to provide in vivo, in situ molecular signatures of disease
such as infection and inflammation. However, enhancing the quality of data
acquired by this technique for better visualization and subsequent analysis
remains a challenging problem. Cross coupling between fiber cores and sparse
sampling by imaging fiber bundles are the main reasons for image degradation,
and poor detection performance (i.e., inflammation, bacteria, etc.). In this
work, we address the problem of deconvolution and restoration of OEM data. We
propose a hierarchical Bayesian model to solve this problem and compare three
estimation algorithms to exploit the resulting joint posterior distribution.
The first method is based on Markov chain Monte Carlo (MCMC) methods, however,
it exhibits a relatively long computational time. The second and third
algorithms deal with this issue and are based on a variational Bayes (VB)
approach and an alternating direction method of multipliers (ADMM) algorithm
respectively. Results on both synthetic and real datasets illustrate the
effectiveness of the proposed methods for restoration of OEM images.
