Articles | Volume 18, issue 21
https://doi.org/10.5194/amt-18-6291-2025
https://doi.org/10.5194/amt-18-6291-2025
Research article
 | 
06 Nov 2025
Research article |  | 06 Nov 2025

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

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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-612', Anonymous Referee #1, 09 May 2025
  • RC2: 'Comment on egusphere-2025-612', Anonymous Referee #2, 23 May 2025
  • RC3: 'Comment on egusphere-2025-612', Anonymous Referee #3, 10 Jun 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Gholamhossein Bagheri on behalf of the Authors (31 Jul 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (12 Aug 2025) by Luca Lelli
AR by Gholamhossein Bagheri on behalf of the Authors (21 Aug 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (22 Aug 2025) by Luca Lelli
AR by Gholamhossein Bagheri on behalf of the Authors (29 Aug 2025)
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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.
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