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.
Abstract (translated)
光学子显微镜（OEM）是一种新兴的技术平台，具有临床前和临床成像应用。通过纤维束的肺部OEM具有提供诸如感染和炎症的疾病的体内原位分子特征的潜力。然而，提高通过该技术获得的数据质量以便更好地可视化和随后的分析仍然是一个具有挑战性的问题纤维芯之间的交叉耦合和通过成像纤维束的稀疏采样是图像劣化和检测性能差（即炎症，细菌等）的主要原因。在这项工作中，我们解决了反卷积和OEM数据恢复的问题。我们提出了一种分层贝叶斯模型来解决这个问题，并比较三种估计算法来利用得到的联合后验分布。第一种方法基于马尔可夫链蒙特卡罗（MCMC）方法，但是，它表现出相对长的计算时间。第二和第三算法处理该问题并且分别基于变分贝叶斯（VB）方法和交替方向乘法器（ADMM）算法。合成数据集和真实数据集的结果说明了所提出的恢复OEM图像方法的有效性。
URL
https://arxiv.org/abs/1701.08107
