Deconvolution and Restoration of Optical Endomicroscopy Images
This work addresses image quality issues in OEM for pulmonary disease detection, but it is incremental as it applies existing Bayesian methods to a specific domain.
The authors tackled the problem of enhancing optical endomicroscopy (OEM) images degraded by cross-coupling and sparse sampling, proposing a hierarchical Bayesian model with three estimation algorithms (MCMC, VB, ADMM) that effectively restored images on synthetic and real datasets.
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.