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    Performance of autofocus capability of deep convolutional neural networks in digital holographic microscopy


    Pitkäaho, Tomi and Manninen, Aki and Naughton, Thomas J. (2017) Performance of autofocus capability of deep convolutional neural networks in digital holographic microscopy. In: Digital Holography and Three-Dimensional Imaging, 29 May – 1 Jun 2017, JeJu Island, South Korea.

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    Official URL: https://doi.org/10.1364/DH.2017.W2A.5


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    Abstract

    Digital holographic microscopy (DHM) is a label-free, single-shot technique that is well suited for imaging living three dimensional samples [1]. DHM overcomes a problem present in conventional optical microscopes of a shallow depth-of-field, permitting one to reconstruct at different in-focus planes of a volume. Nevertheless, an object of interest is usually in-focus only in one or few depths as a single reconstruction layer still carries a shallow depth-of-field.Different methods to find objects in focus have been proposed. These methods can be based on self-entropy [2],amplitude analysis [3], power spectra [4] or other metrics [5]. Common to all of these single wavelength methods is that they reconstruct a stack of images that the focus metric is applied to. This procedure is applied to each of the holograms. We propose to use deep learning as an autofocusing method. The greatest benefit of the proposed method is that after the training is completed, the in-focus plane can be obtained by using only the single in the hologram plane and without any reconstruction.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Digital holography; Three-dimensional image processing;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 12027
    Depositing User: Thomas Naughton
    Date Deposited: 17 Dec 2019 11:45
    Refereed: Yes
    URI:

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