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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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Related Experiment Video

Updated: Jun 12, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Automatic multifocusing in digital holographic microscopy.

Dániel Terbe, László Orzó, Ákos Zarándy

    Optics Express
    |June 11, 2026
    PubMed
    Summary

    This study introduces a two-stage pipeline for automatic multifocusing in digital holographic microscopy (DHM). The method efficiently and accurately determines the 3D positions of multiple microscopic objects within a single hologram.

    Area of Science:

    • Microscopy and Imaging Technologies
    • Computational Optics
    • Biophotonics

    Background:

    • Digital holographic microscopy (DHM) enables numerical refocusing but struggles with multifocusing of objects at varying depths.
    • Existing autofocusing methods in DHM are computationally intensive or limited to single focal planes.
    • Automatic multifocusing for heterogeneous objects in flow-through DHM remains a significant challenge.

    Purpose of the Study:

    • To develop a robust and efficient automatic multifocusing system for in-line flow-through DHM.
    • To address the challenge of localizing objects distributed across multiple axial depths within a single hologram.
    • To enable quasi-real-time 3D positioning of microscopic samples.

    Main Methods:

    • A two-stage pipeline combining a coarse, low-resolution 3D search with a deep-learning-based refinement stage.

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  • Utilizing a robust focus metric on downscaled holograms for initial object detection and approximate positioning.
  • Employing deep learning models (regression and classification) to predict residual defocus for precise axial localization.

    • Main Results:

    • The proposed system achieves accurate, data-efficient, and robust multifocusing.
    • Demonstrated computational efficiency suitable for quasi-real-time DHM applications.
    • Successfully evaluated on a diverse dataset of living micro-organisms.

    Conclusions:

    • The developed two-stage pipeline effectively solves the automatic multifocusing problem in DHM.
    • The system offers a practical solution for analyzing heterogeneous microscopic samples in 3D.
    • This advancement facilitates broader applications of DHM in biological and microfluidic studies.