Deconvolution and Restoration of Optical Endomicroscopy Images

Ahmed Karam Eldaly, Yoann Altmann, Antonios Perperidis, Nikola Krstajic, Tushar R. Choudhary, Kevin Dhaliwal, Stephen McLaughlin

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    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.
    Original languageEnglish
    Pages (from-to)194-205
    Journal IEEE Transactions on Computational Imaging
    Volume4
    Issue number2
    DOIs
    Publication statusPublished - 9 Mar 2018

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    Keywords

    • Optical endomicroscopy
    • Deconvolution
    • Image restoration
    • Irregular sampling
    • Bayesian models

    Cite this

    Karam Eldaly, A., Altmann, Y., Perperidis, A., Krstajic, N., Choudhary, T. R., Dhaliwal, K., & McLaughlin, S. (2018). Deconvolution and Restoration of Optical Endomicroscopy Images. IEEE Transactions on Computational Imaging, 4(2), 194-205. https://doi.org/10.1109/TCI.2018.2811939