# Deconvolution and Restoration of Optical Endomicroscopy Images

## TL;DR

This paper introduces a hierarchical Bayesian framework for deconvolving and restoring optical endomicroscopy images, improving image quality by addressing fiber core cross coupling and sparse sampling issues.

## Contribution

It proposes a novel Bayesian model and compares three estimation algorithms, including MCMC, VB, and ADMM, for OEM image restoration.

## Key findings

- Bayesian methods effectively restore OEM images
- VB and ADMM algorithms reduce computational time
- Restored images show improved visualization and analysis

## Abstract

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.