Articles | Volume 18, issue 21
https://doi.org/10.5194/amt-18-6291-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-6291-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Nov 2025
Research article |
| 06 Nov 2025
| 06 Nov 2025
In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation
Birte Thiede
Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, 37077 Göttingen, Germany
Faculty of Physics, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
Oliver Schlenczek
Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, 37077 Göttingen, Germany
Katja Stieger
Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, 37077 Göttingen, Germany
Faculty of Physics, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
Alexander Ecker
Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, 37077 Göttingen, Germany
Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany
Eberhard Bodenschatz
Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, 37077 Göttingen, Germany
Faculty of Physics, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
Laboratory of Atomic and Solid State Physics, Cornell University, 523 Clark Hall, Ithaca, NY 14853, USA
Gholamhossein Bagheri
CORRESPONDING AUTHOR
Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, 37077 Göttingen, Germany
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Short summary
Accurate measurement of cloud particles is crucial for cloud research. While holographic imaging enables detailed analysis of cloud droplet size, shape, and distribution, processing errors remain poorly quantified. To address this, we developed CloudTarget, a patterned photomask that can quantify the detection efficiency and uncertainties. Additionally, our AI-based classification enhances both accuracy and speed, achieving over 90 % precision while accelerating analysis 100-fold.
Accurate measurement of cloud particles is crucial for cloud research. While holographic imaging...
