In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation

Authors

Gholamhossein Bagheri (ORCID: https://orcid.org/0000-0002-3707-4754)
Eberhard Bodenschatz (ORCID: https://orcid.org/0000-0002-2901-0144)
Alexander S. Ecker (ORCID: https://orcid.org/0000-0003-2392-5105)
Oliver Schlenczek (ORCID: https://orcid.org/0000-0001-7247-465X)
Birte Thiede (ORCID: https://orcid.org/0000-0003-3248-0910)
Katja Stieger

Publication Details

Journal: Atmospheric measurement techniques
Published: 2025-11-06
DOI: https://doi.org/10.5194/amt-18-6291-2025
Primary Topic: Digital Holography and Microscopy
Type: article
Field-Weighted Citation Impact: 0.00

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article

In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation

Gholamhossein Bagheri, Eberhard Bodenschatz, Alexander S. Ecker, Oliver Schlenczek et al.

Nov 6, 2025

Atmospheric measurement techniques

Digital Holography and Microscopy

article

In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation

Gholamhossein Bagheri, Eberhard Bodenschatz, Alexander S. Ecker, Oliver Schlenczek, Birte Thiede, Katja Stieger

November 6, 2025article en

Abstract

No abstract available for this paper.

Atmospheric measurement techniquesVol. 18(21)

Openalex Percentile: Top 74%

Digital Holography and Microscopy

DOI Open Access PDF OpenAlex

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In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation — Gholamhossein Bagheri, Eberhard Bodenschatz, et al. · Atmospheric measurement techniques (2025) | TGRS Research Map | TGRS