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    Focus prediction in digital holographic microscopy using deep convolutional neural networks


    Pitkäaho, Tomi and Manninen, Aki and Naughton, Thomas J. (2019) Focus prediction in digital holographic microscopy using deep convolutional neural networks. Applied Optics, 58 (5). A202-A208. ISSN 0003-6935

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    Abstract

    Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its potential in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of Madin–Darby canine kidney cell clusters encoded in digital holograms. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, without performing numerical propagation. This paper reports on extensions to preliminary work published earlier as one of the first applications of deep learning in the field of digital holographic microscopy.

    Item Type: Article
    Additional Information: Cite as: Tomi Pitkäaho, Aki Manninen, and Thomas J. Naughton, "Focus prediction in digital holographic microscopy using deep convolutional neural networks," Appl. Opt. 58, A202-A208 (2019)
    Keywords: Focus; prediction; digital holographic; microscopy; deep convolutional; neural networks;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14151
    Identification Number: https://doi.org/10.1364/AO.58.00A202
    Depositing User: Thomas Naughton
    Date Deposited: 10 Mar 2021 14:33
    Journal or Publication Title: Applied Optics
    Publisher: Optical Society of America
    Refereed: Yes
    URI:

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